System and method for automated identification of location types for geofences

ABSTRACT

A system and method and system flow for processing GPS event data to identify frequent stop locations and geofences therefor and automatedly categorize them with location types.

DESCRIPTION OF RELATED ART

It is known to provide an on-board unit which uses technology such as GPS (Global Positioning System) to monitor a vehicle's positions and transmit wireless uploads to a central host system as well as manage of incoming data traffic without data losses or corruption and/or without database record locking. Such a unit may also upload vehicle status events such as engine fault events. GB2345824 and U.S. Pat. No. 7,388,518 describes such systems and methods therefor, the entirety of each of which are incorporated by reference herein.

SUMMARY OF THE INVENTION

Disclosed is a computer system including at least one computer processor and computer readable storage medium or media including computer code and at least one storage device in which is stored GPS data for at least one vehicle the system comprising: a memory including a GPS event database including GPS event data transmitted from at least one GPS device, each GPS device associated with a vehicle, and stored over a period of time; the one or more processors programmed at least to: receive GPS event data transmitted from at least one GPS device, each GPS device associated with a vehicle, the event GPS event data including location information for the vehicle; store the GPS event data in a GPS event database operatively coupled to at least one of the processors; analyze the GPS event data to determine vehicle stops for at least one vehicle; determine if the vehicle stops meets a location type identification criterion for a geofence; and if the criterion is met, automatically categorize the geofence. In an embodiment, the geofence can comprise a boundary shape, wherein the boundary shape is at least one of a boundary shape for an automatedly identified frequent stop location, a boundary shape for a user identified point of interest, and a boundary shape for predetermined point of interest. In an embodiment, the one or more processors can be further programmed at least to: automatedly categorize the location type, the location type comprising a location type being selected from the group of: an office location type, a depot location type, and a home location type.

In an embodiment, the one or more processors can be further programmed at least to: determine if the stops meet a location type identification criterion for the area by at least: determining vehicle stop characteristics for the area; and determining if the vehicle stop characteristics for the area meet a location type identification criterion.

In an embodiment, the one or more processors can be further programmed at least to: determine the vehicle stop characteristics by at least: identifying a total number of stops for at least one fleet of vehicles; identifying and sorting the stops in order of the geofences with the highest average percentage of stops of the total stops for each geofence; identifying one or more of the geofences having the highest average percentage of stops as the highest ranked areas; determining the variability of the stops for each of the highest ranked geofences; identifying, for each of the highest ranked areas, the number of stops that are last stop for a vehicle for that vehicle for a time period; and identifying, for each of the highest ranked areas, a unique vehicle count for the stops. The geofences can be boundary shapes for automatedly identified frequent stop locations.

In an embodiment, the identification of a total number of stops for at least one fleet of vehicles can be identified and sorted by a predefined geographical region. In an embodiment, the one or more processors can be further programmed at least to identify 3 to 50 geofences comprising the highest average percentage of stops as the highest ranked areas. In an embodiment, the one or more processors can be further programmed at least to identify 5 to 25 geofences comprising the highest average percentage of stops as the highest ranked areas. In an embodiment, the one or more processors can be further programmed at least to identify 7 to 10 geofences comprising the highest average percentage of stops. In an embodiment, the one or more processors can be further programmed at least to identify 7 geofences comprising the highest average percentage of stops.

In an embodiment, the determination of variability can comprise calculating the standard deviation across the identified highest ranked geofences.

In an embodiment, the one or more processors can be further programmed to determine if the vehicle stop characteristics for the geofence a location type identification criterion by at least: selecting one or more of the identified highest ranked geofences for categorization; and determining if the identified highest ranked geofences meet a threshold number of the location type identification criteria. In an embodiment, the criteria comprise one or more of: (1) a threshold coefficient of variation; (2) a minimum percentage of the stops in the geofence of the total stops for the number of stops identified for the geographic region; (3) the number of stops for the geofence is a minimum number of stops for a predetermined time period (4) at least one of the stops for the geofence is the last stop for a vehicle; and (5) wherein a threshold number of stops are stops per unique vehicles. In an embodiment, the vehicle stop characteristics for the geofence must meet a threshold number of the location type identification criteria. In an embodiment the threshold number location type identification criteria can be in the range of 2 out of 5 criteria to 5 out of 5 criteria. In an embodiment, the number of geofences identified as having the highest number of stops can be 7, and the threshold coefficient of variation can be from at least about 0.5 to at least about 0.7. In an embodiment the number of geofences identified as having the highest number of stops is 7, and the threshold coefficient of variation is at least about 0.5. In an embodiment, the number of geofences identified as having the highest number of stops can be 7, and the threshold coefficient of variation can be at least about 0.7. In an embodiment the minimum percentage of the stops can be at least 10% of the total stops for the number of stops for the fleet. In an embodiment the minimum number of stops for the predetermined time period can comprise at least 2000 stops for retained business of a fleet entity for 1 year or a minimum of 500 stops for 3 months for a new business for a fleet entity, whereby the geofence is identified as an office location type. In an embodiment, the minimum number of spots are 1500 for retained business for 1 year retained, and a minimum number of 200 stops for 3 months for a new business for a fleet entity, whereby the geofence is identified as a depot location type. In an embodiment the threshold number of unique stops can be at least 30 stops per unique vehicle. In an embodiment, the location type criterion can comprise an industry base criterion.

In an embodiment, the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the system can be configured to at least: select only a first highest ranked geofence, whereby the first highest ranked is categorized as an office location type.

In an embodiment, the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the system can be configured to at least: select a first highest ranked geofence to a second highest ranked geofence, whereby the first highest ranked geofence is categorized as an office location type and the second highest ranked geofence is categorized as a depot location type or an office location type. In an embodiment, the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the system can be configured to at least: select a third highest ranked geofence to a sixth highest ranked geofence, whereby the third highest ranked geofence to the sixth highest ranked geofence are categorized as depot location types. In an embodiment, the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the system can be configured to at least: select third highest ranked geofence to the fifth highest ranked geofence, and the third highest ranked to the fifth highest ranked are categorized as depot location types.

In an embodiment, the one or more processors can be further programmed at least to determine if the geofence meets a home location type identification criterion for the geofence by at least: identifying at least one geofence associated with a single vehicle; determining, for each geofence associated with a single vehicle, a number of stops for the geofence, wherein the number of stops over a time period are the last stop of the day for a predetermined time period; determining a duration of time for each of the number of stops over a time period that are the last stop of the day; and wherein the location type criterion comprises criteria including: a minimum number of vehicle stops in the geofence that are the last stop of the day for the single vehicle; a minimum number of vehicle stops in the geofence that are the last stop of the day; and an address designation for a minimum percentage of stops within the geofence is designated as a qualified home address. In an embodiment, the predetermined time period can be at least about a 30-day period. In an embodiment, the minimum number of vehicle stops in the geofence determined to be the last stop of the day is at least 20. In an embodiment, the minimum duration of the stops that are the last stop of the day is 5 hours or less.

In an embodiment, the one or more processors can be further programmed at least to determine if a fleet policy information indicating an entity associated with a fleet permits a home location for a vehicle.

Disclosed is a method comprising, in at least one computer and a computer readable non-transitory storage medium or media including computer code: receiving GPS event data transmitted from a plurality of GPS devices, each GPS device associated with a vehicle; storing in GPS event database operatively coupled to at least one of the processors, the GPS event data; analyzing the GPS event data to derive and record vehicle stops for at least one vehicle; determining if the vehicle stops meet a location type identification criterion for a geofence; and if the criterion is met, automatedly categorize the geofence. In an embodiment the geofence comprises is at least one of geofence for an automatedly identified frequent stop location, a boundary shape for a user identified point of interest, and a boundary shape for predetermined point of interest. In an embodiment the method can further comprise: automatedly categorizing the location type, the location type comprising a location type being selected from the group of: an office location type, a depot location type, and a home location type. In an embodiment the method can further comprise: determining vehicle stop characteristics for the area; and determining if the vehicle stop characteristics for the area meet a location type identification criterion.

In an embodiment the method can further comprise: identifying a total number of stops for at least one fleet of vehicles; identifying and sorting the stops in order of the geofences with the highest average percentage of stops of the total stops for each geofence; identifying one or more of the geofences having the highest average percentage of stops as the highest ranked areas; determining the variability of the stops for each of the highest ranked geofences; identifying, for each of the highest ranked areas, the number of stops that are last stop for a vehicle for that vehicle for a time period; and identifying, for each of the highest ranked areas, a unique vehicle count for the stops. In an embodiment the method can further comprise: the geofences comprising boundary shapes for automatedly identified frequent stop locations.

In an embodiment the identification of a total number of stops for at least one fleet of vehicles can be identified and sorted by a predefined geographical region.

In an embodiment the method can further comprise: identifying 3 to 50 geofences comprising the highest average percentage of stops as the highest ranked areas. In an embodiment the method can further comprise; identifying 5 to 25 geofences comprising the highest average percentage of stops as the highest ranked areas. In an embodiment the method can further comprise: identifying 7 to 10 geofences comprising the highest average percentage of stops. In an embodiment the method can further comprise: identifying 7 geofences comprising the highest average percentage of stops. In an embodiment the determination of variability comprises calculating the standard deviation across the identified highest ranked geofences.

In an embodiment the method can further comprise: selecting one or more of the identified highest ranked geofences for categorization; and determining if the identified highest ranked geofences meet a threshold number of the location type identification criteria. In an embodiment of the method, the criteria comprise one or more of: (1) a threshold coefficient of variation; (2) a minimum percentage of the stops in the geofence of the total stops for the number of stops identified for the geographic region; (3) the number of stops for the geofence is a minimum number of stops for a predetermined time period (4) at least one of the stops for the geofence is the last stop for a vehicle; and (5) wherein a threshold number of stops are stops per unique vehicles.

In an embodiment of the method the vehicle stop characteristics for the geofence the geofence must meet a threshold number of the location type identification criteria. In an embodiment of the method the threshold number location type identification criteria can be in the range of 2 out of 5 criteria to 5 out of 5 criteria. In an embodiment of the method the number of geofences identified as having the highest number of stops is 7, and the threshold coefficient of variation is at least 0.5. In an embodiment of the method the number of geofences identified as having the highest number of stops is 7, and the threshold coefficient of variation is at least 0.7.

In an embodiment of the method minimum percentage of the stops is at least 10% of the total stops for the number of stops for the fleet. In an embodiment of the method the minimum number of stops for the predetermined time period comprises at least 2000 stops for retained business of a fleet entity for 1 year or a minimum of 500 stops for 3 months for a new business for a fleet entity, whereby the geofence is identified as an office location type. In an embodiment of the method the minimum number of spots 1500 for retained business for 1 year retained, and a minimum number of 200 stops for 3 months for a new business for a fleet entity, whereby the geofence is identified as a depot location type. In an embodiment of the method the threshold number of unique stops is at least 30 stops per unique vehicle.

In an embodiment of the method the location type criterion comprises an industry base criterion. In an embodiment of the method, the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the method further comprises: selecting only a first highest ranked geofence, whereby the first highest ranked is categorized as an office location type. In an embodiment of the method the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the method further comprises: selecting a first highest ranked geofence to a second highest ranked geofence, whereby the first highest ranked geofence is categorized as an office location type and the second highest ranked geofence is categorized as a depot location type or an office location type. In an embodiment of the method the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the method further comprises: selecting a third highest ranked geofence to a sixth highest ranked geofence, whereby the third highest ranked geofence to the sixth highest ranked geofence are categorized as depot location types. In an embodiment of the method the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the method further comprises: selecting third highest ranked geofence to the fifth highest ranked geofence, and the third highest ranked to the fifth highest ranked are categorized as depot location types.

In an embodiment of the method the method further comprises determining if the geofence meets a home location type identification criterion for the geofence by at least: identifying at least one geofence associated with a single vehicle; determining, for each geofence associated with a single vehicle, a number of stops for the geofence, wherein the number of stops over a time period are the last stop of the day for a predetermined time period; determining a duration of time for each of the number of stops over a time period are that are the last stop of the day; and wherein the location type criterion comprises criteria including: a minimum number of vehicle stops in the geofence that are the last stop of the day for the single vehicle; a minimum number of vehicle stops in the geofence that are the last stop of the day; and an address designation for a minimum percentage of stops within the geofence is designated as a qualified home address. In an embodiment of the method the predetermined time period is at least about a 30-day period. In an embodiment of the method the minimum number of vehicle stops in the geofence determined to be the last stop of the day is at least 20. In an embodiment of the method the minimum duration of the stops that are the last stop of the day is 5 hours or less. In an embodiment of the method determining if a fleet policy information indicating an entity associated with a fleet permits a home location for a vehicle.

Throughout the specification and claims, the following terms take at least the meanings explicitly associated herein, unless the context dictates otherwise. The meanings identified below do not necessarily limit the terms, but merely provide illustrative examples for the terms. The phrase “an embodiment” as used herein does not necessarily refer to the same embodiment, though it may. In addition, the meaning of “a,” “an,” and “the” include plural references; thus, for example, “an embodiment” is not limited to a single embodiment but refers to one or more embodiments. Similarly, the phrase “one embodiment” does not necessarily refer the same embodiment and is not limited to a single embodiment. As used herein, the term “or” is an inclusive “or” operator, and is equivalent to the term “and/or,” unless the context clearly dictates otherwise.

It is noted that in this disclosure and particularly in the claims and/or paragraphs, terms such as “comprises,” “comprised,” “comprising,” and the like can have the meaning attributed to it in U.S. patent law; that is, they can mean “includes,” “included,” “including,” “including, but not limited to” and the like, and allow for elements not explicitly recited. Terms such as “consisting essentially of” and “consists essentially of” have the meaning ascribed to them in U.S. patent law; that is, they allow for elements not explicitly recited, but exclude elements that are found in the prior art or that affect a basic or novel characteristic of the invention. Embodiments of the present invention are disclosed or are apparent from and encompassed by, the following description.

It is to be understood that the figures and descriptions of the present invention have been simplified to illustrate elements that are relevant for a clear understanding of the present invention, while eliminating, for purposes of clarity, many other elements which are conventional in this art. Those of ordinary skill in the art will recognize that other elements are desirable for implementing the present invention. However, because such elements are well known in the art, and because they do not facilitate a better understanding of the present invention, a discussion of such elements is not provided herein.

The use of the terms “a,” “an,” “at least one,” “one or more,” and similar terms indicate one of a feature or element as well as more than one of a feature. The use of the term “the” to refer to the feature does not imply only one of the feature and element.

When an ordinal number (such as “first,” “second,” “third,” and so on is used as an adjective before a term, that ordinal number is used (unless expressly or clearly specified otherwise) merely to indicate a particular feature, such as to distinguish that particular feature from another feature that is described by the same term or by a similar term.

When a single device, article or other product is described herein, more than one device/article (whether or not they cooperate) may alternatively be used in place of the single device/article that is described. Accordingly, the functionality that is described as being possessed by a device may alternatively be possessed by more than one device/article (whether or not they cooperate). Similarly, where more than one device, article or other product is described herein (whether or not they cooperate), a single device/article may alternatively be used in place of the more than one device or article that is described. Accordingly, the various functionality that is described as being possessed by more than one device or article may alternatively be possessed by a single device/article.

The functionality and/or the features of a single device that is described may be alternatively embodied by one or more other devices which are described but are not explicitly described as having such functionality/features. Thus, other embodiments need not include the described device itself, but rather can include the one or more other devices which would, in those other embodiments, have such functionality/features.

The present invention will now be described in detail on the basis of exemplary embodiments. The invention disclosed herein may be practiced using programmable digital computers and networks therefor.

It will be appreciated by those skilled in the art that the foregoing brief description and the following detailed description are exemplary (i.e., illustrative) and explanatory of the present invention, but are not intended to be restrictive thereof or limiting of the advantages which can be achieved by this invention in various implementations. Additionally, it is understood that the foregoing summary and ensuing detailed description are representative of some embodiments of the invention, and are neither representative nor inclusive of all subject matter and embodiments within the scope of the present invention. Thus, the accompanying drawings, referred to herein and constituting a part hereof, illustrate embodiments of this invention, and, together with the detailed description, serve to explain principles of embodiments of the invention.

DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated in the figures of the accompanying drawings, which are meant to be exemplary and not limiting, and in which like references are intended to refer to like or corresponding things.

FIGS. 1A-1D are block diagrams of a representative computer system.

FIG. 2 depicts a representative GPS system.

FIGS. 3A-3E depicts exemplary representations for graphic user interfaces.

FIGS. 4-7 are flowcharts depicting exemplary processes for automated identification and updating frequent stop locations.

FIGS. 8-9 are a flowcharts depicting exemplary processes for automated identification of location types for frequent stop locations.

FIG. 10 is a graph reflecting a geofence analysis.

FIGS. 11A-11B are graphs reflecting a geofence analysis.

FIGS. 12A-12C show exemplary representations for graphic user interfaces for correcting geofences.

FIGS. 13A-13K show exemplary representations for graphic user interfaces for correcting geofences.

FIGS. 14A-14C show an exemplary flow showing GPS event data analysis.

DETAILED DESCRIPTION OF THE EMBODIMENTS

As shown in FIGS. 1A-1B, disclosed is a system 100, which includes a computer 140 containing a processor 145, memory 157 and other components typically present in general purpose computers.

FIG. 1A is a block diagram of a representative computer. The computer system 140 includes at least one processor 145, such as an Intel Core™ or Xeon™ series microprocessor or a Freescale™ PowerPC™ microprocessor, coupled to a communications channel 147. The computer system 140 further includes an input device 149 such as, e.g., a keyboard or mouse, an output device 151 such as, e.g., a CRT or LCD display, a communications interface 153, a data storage device 155 such as a magnetic disk or an optical disk, and memory 157 such as Random-Access Memory (RAM), each coupled to the communications channel 147. The communications interface 153 may be coupled to a network such as the Internet.

Memory 157 stores information accessible by processor 145, including instructions that may be executed by the processor 145. It also includes data that may be retrieved, manipulated or stored by the processor. The memory may be of any type capable of storing information accessible by the processor, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories. The processor 145 may be any well-known processor, such as processors from Intel Corporation or AMD. Alternatively, the processor may be a dedicated controller such as an ASIC.

The instructions may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. In that regard, the terms “instructions,” “steps” and “programs” may be used interchangeably herein. The instructions may be stored in object code format for direct processing by the processor, or in any other computer language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance. Functions, methods and routines of the instructions are explained in more detail below.

One skilled in the art will recognize that, although the data storage device 155 and memory 157 are depicted as different units, the data storage device 155 and memory 157 can be parts of the same unit or units, and that the functions of one can be shared in whole or in part by the other, e.g., as RAM disks, virtual memory, etc. It will also be appreciated that any particular computer may have multiple components of a given type, e.g., processors 145, input devices 149, communications interfaces 153, etc.

The data storage device 155 and/or memory 157 may store an operating system 160 such as Microsoft Windows 7®, Windows XP® or Vista™, Linux®, Mac OS®, or Unix®. Other programs 162 may be stored instead of or in addition to the operating system. It will be appreciated that a computer system may also be implemented on platforms and operating systems other than those mentioned. Any operating system 160 or other program 162, or any part of either, may be written using one or more programming languages such as, e.g., Java®, C, C++, C#, Visual Basic®, VB.NET®, Perl, Ruby, Python, or other programming languages, possibly using object oriented design and/or coding techniques.

Data may be retrieved, stored or modified by processor 145 in accordance with the instructions. For instance, although the system and method is not limited by any particular data structure, the data may be stored in computer registers, in a relational database as a table having a plurality of different fields and records, XML documents, or flat files. The data may also be formatted in any computer-readable format such as, but not limited to, binary values, ASCII or Unicode. By further way of example only, image data may be stored as bitmaps comprised of pixels that are stored in compressed or uncompressed, or lossless or lossy formats (e.g., JPEG), vector-based formats (e.g., SVG) or computer instructions for drawing graphics. Moreover, the data may comprise any information sufficient to identify the relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories (including other network locations) or information that is used by a function to calculate the relevant data.

It will be understood by those of ordinary skill in the art that the processor and memory may actually comprise multiple processors and memories that may or may not be stored within the same physical housing. For example, some of the instructions and data may be stored on removable CD-ROM and others within a read-only computer chip. Some or all of the instructions and data may be stored in a location physically remote from, yet still accessible by, the processor. Similarly, the processor may actually comprise a collection of processors which may or may not operate in parallel.

One skilled in the art will recognize that the computer system 140 may also include additional components and/or systems, such as network connections, additional memory, additional processors, network interfaces, input/output busses, for example. One skilled in the art will also recognize that the programs and data may be received by and stored in the system in alternative ways. For example, a computer-readable storage medium (CRSM) reader 164, such as, e.g., a magnetic disk drive, magneto-optical drive, optical disk drive, or flash drive, may be coupled to the communications bus 147 for reading from a computer-readable storage medium (CRSM) 166 such as, e.g., a magnetic, disk, a magneto optical disk, an optical disk, or flash RAM. Accordingly, the computer system 140 may receive programs and/or data via the CRSM reader 164. Further, it will be appreciated that the term “memory” herein is intended to include various types of suitable data storage media, whether permanent or temporary, including among other things the data storage device 155, the memory 157, and the CSRM 166.

Two or more computer systems 140 may be connected, e.g., in one or more networks, via, e.g., their respective communications interfaces 153 and/or network interfaces (not depicted).

A computer system network is shown in FIG. 1B. A network 182 may, for example, connect one or more workstations 184 with each other and with other computer systems, such as file servers 186 or mail servers 188. The connection may be achieved tangibly, e.g., via Ethernet® or optical cables, or wirelessly, e.g., through use of modulated microwave signals according to the IEEE 802.11 family of standards. A computer system that participates in the network may send data to another computer system in the network via the network connection.

One use of a network 182 is to enable a computer system to provide services to other computer systems, consume services provided by other computer systems, or both. For example, a file server 186 may provide common storage of files for one or more of the workstations 190 on a network 182. A workstation 190 sends data including a request for a file to the file server 186 via the network 182 and the file server 186 may respond by sending the data from the file back to the requesting workstation 190.

As will be recognized by those skilled in the relevant art, the terms “workstation,” “client,” and “server” are used herein to describe a computer's function in a particular context. A workstation may, for example, be a computer that one or more users work with directly, e.g., through a keyboard and monitor directly coupled to the computer system. A computer system that requests a service through a network is often referred to as a client, and a computer system that provides a service is often referred to as a server. But any particular workstation may be indistinguishable in its hardware, configuration, operating system, and/or other software from a client, server, or both.

In one aspect, computer 204 is a server communicating with one or more client computers 184,192. For example, computer 204 may be a web server or a hub and data storage service. Each client computer may be configured similarly to the server 184, 192, with a processor, memory and instructions. Each client computer 184, 192 may be a personal computer, intended for use by a person, having all the internal components normally found in a personal computer such as a central processing unit (CPU) 145, display device 151 (for example, a monitor having a screen, a projector, a touch-screen, a small LCD screen, a television, or another device such as an electrical device that is operable to display information processed by the processor), CD-ROM, hard-drive, user input 149 (for example, a mouse, keyboard, touch-screen or microphone), speakers, modem and/or network interface device (telephone, cable or otherwise) and all of the components used for connecting these elements to one another. Moreover, computers in accordance with the systems and methods described herein may comprise any device capable of processing instructions and transmitting data to and from humans and other computers including general purpose computers, PDAs, network computers lacking local storage capability, and set-top boxes for televisions.

Although the client computers 184, 192 may comprise a full-sized personal computer, the system and method may also be used in connection with mobile devices capable of wirelessly exchanging data with a server over a network such as the Internet. For example, client computer 184, 192 may be a wireless-enabled PDA such as an iPhone, an Android enabled smart phone, a Blackberry phone, or another Internet-capable cellular phone. In either regard, the user may input information using a small keyboard (in the case of a Blackberry phone), a keypad (in the case of a typical cell phone), a touch screen (in the case of a PDA and/or smart phone) or any other means of user input.

Client computers 184, 192 may include a component, such as circuits, to determine the geographic location of the device. For example, mobile device may include a GPS receiver. By way of further example, the component may include software for determining the position of the device based on other signals received at the mobile device, such as signals received at a cell phone's antenna from one or more cell phone towers if the mobile device is a cell phone.

Servers 186, 188, 202, 204 and client computers 184 and 192 are capable of direct and indirect communication, such as over a network 180, 200. Although only a few computers are depicted in FIGS. 1A-1B, it should be appreciated that a typical system can include a large number of connected computers, with each different computer being at a different node of the network 200. The network, and intervening nodes, may comprise various configurations and protocols including the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP, and various combinations of the foregoing. Such communication may be facilitated by any device capable of transmitting data to and from other computers, such as modems (e.g., dial-up, cable or fiber optic) and wireless interfaces.

Although certain advantages are obtained when information is transmitted or received as noted above, other aspects of the system and method are not limited to any particular manner of transmission of information. For example, in some aspects, information may be sent via a medium such as a disk, tape or CD-ROM. In other aspects, the information may be transmitted in a non-electronic format and manually entered into the system. Yet further, although some functions are indicated as taking place on a server and others on a client, various aspects of the system and method may be implemented by a single computer having a single processor.

A network 182 may be connected to one or more other networks 180, e.g., via a router 196. A router 196 may also act as a firewall, monitoring and/or restricting the flow of data to and/or from a network 180 as configured to protect the network. A firewall may alternatively be a separate device (not pictured) from the router 196.

A network of networks 180 may be referred to as an internet. The term “the Internet” 200 refers to the worldwide network of interconnected, packet-switched data networks that uses the Internet Protocol (IP) to route and transfer data. A client and server on different networks may communicate via the Internet 200. For example, a workstation 190 may request a World Wide Web document, interface or other object from a Web Server 202. The Web Server 202 may process the request and pass it to, e.g., an Application Server 204. The Application Server 204 may then conduct further processing, which may include, for example, sending data to and/or receiving data from one or more other data sources. Such a data source may include, e.g., other servers on the same network.

For example in one embodiment, an on-board GPS unit uploads information (eg. via a hub 153) about a vehicle v1, v2 . . . vn to a central host system 208. Information about the vehicle derived from the GPS information can be presented as a graphic user interface to a user on a display device 151, for example, as a layout shown in FIG. 2.

In one embodiment, the system is programmed at least to receive GPS event data recorded by a GPS (Global Positioning System) device, for example, using an on-board unit which uses technology such as GPS to monitor a vehicle's positions and transmit wireless uploads to a central host system. Referring to FIG. 2 a vehicle tracking system comprises on-board units 1 in vehicles v1, v2, v3 . . . vn, which communicate wirelessly via mobile networks 2 to gateways 3. In this diagram two wireless protocols are indicated, namely GPRS and SMS. However there are typically a variety of additional protocols. The gateways 3 communicate using protocols such as UDP and TCP via the Internet 4 with a host system 7 having receivers 5 which are operating system services, and a data storage system 208. The incoming data is written from the receivers 5 to the data storage system 208, which includes a communication hub 153 and database 208. GB2345824 and U.S. Pat. No. 7,388,518 describes such systems and methods therefor, the entirety of each of which are incorporated by reference herein.

FIG. 1C is a block diagram of a host system 7 showing an exemplary system configuration for a host system 7. As shown therein, a communications layer 153 is operable to receive incoming GPS data and write data from the receivers 5 to the data storage system 208. The data storage system can be divided into any number of databases and logical layers for data analysis and storage. For example, a messaging layer 208 b can be configured to store GPS event data from GPS on-board units 1 in a GPS event database 211. A separate routing database 212 could be provided to store mapping and route data based on, inter alia, GPS data to store routes traveled by vehicles. Another database layer 208 a can include a business logic database 204 and a reporting database 210 to store rules for analysis of data and reports analyzed data respectively.

A database layer 208 b can be operably connected to other databases, for example external databases 214. For example the system can be operatively connected to at least one speed database including mapping data.

For example, an external database 214 can provide mapping data, which can be used in conjunction with GPS data 211 and business logic 204 to identify or create reports as to frequent stop locations. An applications layer 204, such as an application server 204, can be used to run applications processing, which may include, for example, sending data to and/or receiving data from one or more other data sources such as client workstations, as described above. For example the application layer 214 can be used to provide a graphic user interface 151 of a client workstation 184,190 a user-interactive interface.

In another example an external database 214 can provide mapping data which provides data for addresses and mapping.

An applications layer 204, such as an application server 204, can be used to run applications processing, which may include, for example, sending data to and/or receiving data from one or more other data sources such as client workstations 184, 190, as described above. For example the application layer 214 can be used to provide a graphic user interface 151 of a client workstation for a user-interactive interface.

In one embodiment, a system is configured to receive store, and process GPS data to provide to a graphic user interface of a client a user-interactive interface for tracking and reporting on vehicles and vehicle fleets. For each vehicle, GPS event data is stored for over an operation period. For example, the data can be stored and processed to show event data for at least one vehicle v for an operation period of a workday, a week, a month, a quarter, a year, the life of a service contract, or any desirable time period. The GPS event data can then be analyzed to derive a plurality of operational metrics for each vehicle. Exemplary operational metrics that can be derived from GPS data include: engine on/off, stop duration, vehicle mileage, idling, number of stops, distance travelled, and speed (including high speed and average speed). For example, an on-board GPS device can be configured to be operational to transmit when a vehicle engine is on, thus engine on/off time can be derived. Idling (stopped while engine running) and speeding (distance/time), as well as vehicle mileage can also be derived from tracking via GPS. This data in turn can be used to derive a number of other operational metrics, including vehicle activity over a predetermined time period, vehicle operational reports, employee performance (e.g., working hours, deliveries per day), driver behavior (e.g.: speeding violations, idling over limits), and fleet performance (e.g., metrics based on data above derived for multiple vehicles). Accordingly any number of operational metrics can be derived from GPS data, either alone or in conjunction with data from other databases, including number of stops per vehicle, duration of stops, performance against a criterion, employee performance, driver behavior, and fleet performance, speeding severity, a speeding violation, average vehicle speed, vehicle speed versus a posted speed limit, vehicle speed versus a speed threshold, and an average road speed for a fleet or driver speed for a route versus average road/traffic speed for a route. A graphic user interface can be configured to display including a representation of at least one operational metric for each of a plurality of vehicles. In one embodiment a stop can include complete stop definition known in the art. In one embodiment a stop definition can also include idling for 2 minutes.

Exemplary graphic representations or interfaces 300 for a graphic user interface for a user is shown in FIGS. 3A-3C. Such interfaces could be in the form of application software for computer and digital devices as described above, or in the form of webpage accessed by a client from at least one host server, or any combinations thereof as broadly disclosed herein and without limitation. As shown in FIG. 3A, a “Dashboard” 320 page gives a user a first interactive screen to view GPS data and operational metrics. The top of the graphic user interface 300 has, in one embodiment, tabs for “Reports” 322, “Live Fleet,” 324 “Route Replay,” 326 “Alerts,” 328 “Fleet Service,” 330 and “Admin” 332, which lead to other user interactive displays. Of course any number of tabs or links could be configured for the features as described herein, for example for “Speed” “Trending,” “Score Card,” and/or a “MyFleetmatics” or “My Fleet” page which can include user configurable fleet statistics and alerts, or can have default statistics personal to a user, or some mix of user configurable and default features and statistics.

For example, as shown in FIG. 3C in one embodiment a user can select “Live Fleet” 324 using an input such as a keyboard or a mouse, which would lead to a page with GPS data and mapping software which tracks vehicles v1, v2 . . . vn. The page can allow a user such as a dispatcher to, for example, locate and dispatch the closest vehicle to any job site and reroute the nearest vehicle.

The Dashboard 320, as with any other screen, can be configured to offer preset modules or objects for a user to interact with or view, or in the alternative it can be configured to allow a user to customize the information, reports, alerts, etc most important to the user.

As shown in FIG. 3A, the Dashboard 320 screen includes various graphic representations of operational metrics, including reports and alerts, to aid a user in, among other things, vehicle, employee, and fleet management. For example, the operational metrics 302 for individual vehicles include operational metrics such as a engine on/off 302 a, vehicle mileage 302 b, idling, 302 c, and speeding 302 d. Other operational metrics could also be shown such as average speed, number of stops, speeding severity, and speeding violations. The Dashboard 320 shows at the top of the interface 300 a graphic information display for an individual vehicle 301, which can be selected from a drop down menu 301 a. Other methods of selection can be used, for example, by selecting with a mouse, a graphic for the vehicle v4 (shown as “Van 1006”) in a fleet report graphic 335. The graphic information display for the vehicle 301 includes a reporting and alerts for the vehicle v4, for example, operational metrics such as at least one engine on/off graphic 302 a, vehicle mileage graphic 302 b, idling graphic 302 c, and a speeding graphic 302 d.

As shown, the graphics 302 a, 302 b, 302 c, 302 d on the individual vehicle 301 is shown reporting graphic that shows a rating 303 under each operational metric comparing the vehicle performance against other vehicles in the fleet for a 24 hour period. For instance, an engine on/off rating graphic 303 a puts the vehicle v4 in 4^(th) place in the fleet, a vehicle mileage rating graphic 303 b puts the vehicle v4 in 6^(th) place, idling rating graphic at 303 c at 19^(th) place, and a speeding rating graphic 303 d puts the vehicle v4 at 7^(th) place.

The interface 300 also provides, for the graphic user interface, an alert when an operational metric for a vehicle exceeds a predetermined threshold established for the operational metric. For example, report graphic on the individual vehicle 301 shows the name of the driver, that the vehicle has shutdown (e.g., for the end of the workday), the time for shutdown, and for each of the operational metrics, a pie chart graphic 305 a, 305 b, 305 c, 305 d, and a split-window graphic 306 a/308 a, 306 b/308 b, 306 c/308 c, 306 d/308 d/309 d for the individual's engine on/off, vehicle mileage, idling, and speeding graphics respectively, each of which are designed to alert and report to a user when a vehicle has exceeded a predetermined criterion such as a predetermined threshold. Each of the pie charts 305 and split window graphics 306/308 show the time the vehicle spent within threshold 306, as well as a representation that alerts when the vehicle exceeded the threshold. For example, each metric has in the split window graphic 306/308 a predetermined criterion or threshold 306 established for the 24 hour period: 8 hours for engine on/off 306 a, 150 miles for vehicle mileage 306 b/308 b, 1.0 hours for idling 306 c/308 c, and three speeding thresholds of over 70 mph, 80 mph, and 90 mph under speeding 306 d/308 d/309 d.

For example, the “Engine On/Off” split window 306 a/308 a has in a left-hand window, for vehicle operation within the predetermined threshold 306 a, a graphic which includes a color (green) and text describing the vehicle's v4 operation statistic under the threshold, 7 hours, 50 m. The right-hand window 308 a, shows a window with the time remaining in the 24 hour period (16 hours, 10 min.) Because this is time where the engine was not running, and thus within the threshold, the window 308 a is configured to show neutral color or identifier (e.g. white, tan, clear). The pie chart 305 a shows a visual with green and neutral coloring corresponding to the times in the split window 306 a,308 a which allows a user to readily visualize the percentages for the Engine On/Off time.

The “Vehicle Mileage” split window 306 b/308 b shows the predetermined threshold of 150 miles in the left-hand window 306 b, which is in green as with Engine On/Off. However, the right-hand window 308 b shows the driver has exceeded the threshold by another 28 miles. As this is in excess of the threshold, where the window 308 b is configured to alert the user with a red color. The pie chart 305 b shows a visual with green and red coloring corresponding the times in the split window 306 b, 308 b which allows a user to readily visualize the percentages for the vehicle mileage, as well as alert the user that the vehicle is in excess of the threshold.

The Idling and Speeding graphics are similarly configured, thereby proving a user friendly view for all the metrics for the individual vehicle and driver. For example, Idling 302 c includes an alerting threshold where of 1 hour, and alerts for excess of an hour of idling. Speeding 302 d includes reporting and alerting for, among other things, speeds in excess of 70 mph 306 d, 80 mph 308 d, and 90 mph 309 d, with different colors for each (e.g. green, yellow, and red).

It will be understood that other graphical displays could be used, such as bar graphs, gauge icons, whimsical graphics (e.g., a speedometer or a stopwatch), or any other such graphic as is useful.

For each vehicle v1, v2, v3 . . . vn, historical GPS event data can be stored, as for example, for a plurality of operation periods. Accordingly, while the graphics 302 a, 302 b, 302 c, 302 d on the individual vehicle 301 are over an operational 24 hour period, the graphics could be configured to show data for longer periods and/or a plurality of operational periods, such as a week, a month, a quarter, a year, or other periods as desired. Other thresholds could be implemented for each of these periods, as for example, by adding the criteria or thresholds for each operational period, e.g., for Vehicle Mileage 302 b, or an 160 hour threshold for a 4 work-week period (a five day week), where each 24 hour period is 8 hours. Other reports can be generated based on the historical event data.

For example, report information can include one or more reports on: frequent stop locations or “Hotspots,” vehicle activity over a predetermined time period, speed (including high speed and average speed), number of stops, idling, vehicle operational reports, maintenance, employee performance, driver behavior; and fleet performance.

An exemplary, selectable “Top 10” fleet report 335 shows reports 335 a, 335 b, 335 c, 335 d for the top ten vehicles in each of operational metrics for a fleet of vehicles: Engine On/Off 335 a, Distance Traveled 335 c, Idling Report 335 b, and High Speed Report 335 d. As noted herein additional metrics for “Speeding Violations and “Speeding Severity” can be added to the reports relating to speed. Operational metrics for one of the vehicles v4, “Van 1006” is shown at the top of the graphic user interface 301, and the vehicle rankings as shown for v4 are shown in three of the metrics 335 a, 335 c, and 335 d where the vehicle is in the top 10, as described above. While the report shows the “Top 10,” a selectable drop down menu 311 allows a user to select any number of options for reporting (e.g., top 20, 50) and another drop down menu 310 allows a user to select time periods (e.g. 24 hours, 5 days, a month), to obtain ranked vehicle performance for the fleet and vehicles therein.

The “Engine On/Off Report” 335 a ranks the vehicles v1 . . . v10 from highest to lowest for “Engine On” time over the 24 hours. For example, a row for the top vehicle v1 shows the engine was on for 9 hours and 58 minutes and of for 15 hours and 2 minutes. The lowest ranked v10 (Vehicle 0435) shows engine operation for 6 hours and 8 minutes, whereas the off time is 17 hours and 52 minutes. A fillable bar graph shows on and off times, with “on” being green and off being blank or neutral, with the fill line visually showing the percentage of the 24 hour period. Text graphics write out the time. The rows of bar graphs for each vehicle are aligned in a columnar format so as to readily compare each vehicle's statistics with one another.

The “Distance Traveled” 335 c reports and alerts are consistent with criteria for the “Vehicle Mileage” 302 b for the individual driver described above, and ranks vehicles within the top 1.0 of the fleet from highest to lowest for distance traveled. A fillable bar graph shows the predetermined threshold of 150 miles in the left-hand bar graph, which is in green. However, the left-hand of the bar graph shows, with fill lines by percentage, where the driver has exceeded the 150 miles threshold. The top ranked vehicle of the fleet is in excess of the threshold by 234 miles, whereas the 10^(th) ranked vehicle (Van 1026) is only a few miles over. A fillable bar graph shows distances times, with the 150 mile threshold being green any excess mileage being red so as to alert the user, with the fill line visually showing the percentage of the 300 mile distance. Text graphics write out mileage at or under 150 miles on the left-hand side, and mileage in excess right hand side. Again, rows of the bar graphs for each vehicle are aligned columnar format so as to readily compare each vehicle's statistics with one another. The “Idling” 335 b and “High Speed” 335 d reports graphics are similarly configured, thereby proving a user friendly view for all the metrics for the ranked vehicles and drivers in the fleet. For example, “Idling” 335 b includes an alerting threshold of 1 hour, and reports, bar graphs, alerts for excess of an hour of idling. A report (not shown) can also be configured in a format for delivery to a PDA or smartphone, which shows bar graphs, rankings, and idling times for a plurality of drivers/vehicles over a 24 hour period.

FIG. 3B shows another example of a detailed report, as could be accessed, for example, in a page from the “Reports” (see FIG. 3A 322). The report is a “Hotspots Report” 350 for providing, for a graphic user interface, a representation including a representation of the frequent stop locations called “Hotspots,” as shown in the lower table 340 portion of the graphic user interface. The graphic user interface, a representation comprises at least one of a geographic criterion for at least one frequent stop location, a time period criterion for at least one frequent stop location, a rank order; and a distance.

An upper window 342 includes an area for selecting a time period 345 for the report, selecting the number of frequent stop locations 346, selecting a distance for flagging nearby or associated frequent stop locations 347 and a selecting a geographical criterion 348. In one exemplary embodiment the time period criteria can be configured as follows:

Start Month 345 a—defaults to the previous month. If it is the middle of a month, then the system will show the previous month. (e.g.: If today's date is February 15th, then the system will load January by default.)

End Month 345 b—defaults to the previous month; if it is the middle of a month, then the system will show will show the previous month. (e.g.: If today's date is February 15^(th), then the system will load January by default.)

Other criteria for the report include:

Show top (number) Hotspots 346. The dropdown menu can be configured to show a predetermined number of frequent stop locations, for example: 10, 20, 50, 100, 200 or 500.

Flag POI's within (KMs/Miles) 347. The dropdown menu can be configured to show a predetermined number of user-identified POIs (a user identified “Point of Interest”) within a given radius of a frequent stop location, for example—0.1, 0.2, 0.5 or 1.0.

Geography 348. The dropdown menu can be configured to show dropdown listings for predetermined geographical areas, for example, states in the United States of America.

The system can be configured to run the report once a user has selected the criteria for the report, for example by presenting an interface input 351 for to run the report. In the embodiment, the GUI presents a Summary of the criteria for the report in a summary area 352 which shows the selected criteria for Start Month, and Month, Top (number) Hotspots, Flag POIs within (KMs/Miles), and Geography.

Each location is given a row 340 a, 340 b, 340 c, 340 d, 340 e, 340 f, 340 g on the report, and the table 340 which includes columns for a frequent stop location (“Hotspot”) address 343 a, overall rank 343 b, number of visits (“# Visits”) 343 c, number of vehicles making the stops 343 d, average stop duration 343 e, average arrival time 343 f, average travel time 343 g, average distance travelled 343 h, and a frequent stop location name associated with or nearby the location 343 i.

Hotspot Name 343 a—the address of the Hotspot. The “Hotspot” address 343 a includes the address identified and stored as a relevant address for an automatedly identified frequent stop location as described herein. In an embodiment, the address does not include any street/house numbers. This applies when saving frequent stop location “Hotspots” as described below with respect to FIGS. 4-7. In one embodiment, the address can be configured as a link with an additional [+] symbol load Hotspot details (see FIG. 3D). The frequent stop locations are ranked in order from highest to lowest for a time period selected in the upper window, with the highest ranking visit being 350 visits 344 a, the second highest below that at 300 visits 344 b and so on to the lowest rank of 50 visits 344 g. The report includes:

Overall Rank 343 b—displays the Hotspot's overall ranking (regardless of date range). Ranking is based on a total number of stops as described herein. As will be noted, the overall rank 343 b of the frequent stop location may be different than that of the selected period selected in the time area 345 or other criterion for the report as described below.

# Visits 343 c—the number of times the frequent stop location stop was visited during the selected date range. It will order the results. A graphical representation will display beside the number in descending order. The “# Visits” includes a bar graph 344 a, 344 b, 344 c, 344 d, 344 e, 344 f, 344 g which shows a colored fill bar and text showing the number of visits.

Vehicles 343 d—the number of unique vehicles that visited the frequent stop location.

Avg. Stop Duration 343 e—average time vehicles spent at the stop (shutdown duration to Startup).

Avg. Arrival Time 343 f—average time vehicles arrived at the stop (shutdown).

Avg. Travel Time 343 g—average time it took vehicle to get to the stop (last startup to shutdown).

Avg. Distance Travelled 343 h—average distance covered to get to Hotspot.

POIs 343 i—displays nearby POIs—a user identified “Point of Interest” saved by a user—based on the “radius selected” 347. In an embodiment the system is configured to show the top 3 POIs near a Hotspot. For example, a POT within 0.1 miles of a Hotspot would generally correlate with the address of automatedly derived frequent stop location identified as a Hotspot using embodiments described herein, although in certain instances multiple frequent stop locations may correlate with a single address, as for instance where many businesses or people occupy the same building and have the same address. However, the criterion can be set to cover different distances to correlate to addresses in the region where stops are being made by vehicles. In another embodiment, the system can be configured to use a stop as a seed, and can list not only the POIs associated with the automated frequent stop location, but also other nearby addresses (for example, from an external database). Also, it will be appreciated that while geofences for automatedly identified frequent stop locations or Hotspots are automatedly identified points of interest, certain embodiments distinguish user-identified POIs from points of interest from automatedly identified frequent stop locations by calling the former “points of interest” or “POIs” and the latter “Hotspots.”

The report could also be configured to include alerting icons (not shown), which show exceptions, for example where an average stop duration or average arrival time have exceeded predetermined thresholds or criteria.

The system can also be configured to provide mapping and tracking functions, as described above. The system can be configured to interface with mapping systems, for example, such as Google Maps (http://maps.google.com/maps), www.mapquest.com, www.mapsonus.com, www.maps.expedia.com, www.maps.yahoo.com (accessed through www.yahoo.com), www.maps.com, www.maps.excite.com, (accessed through www.excite.com), and www.mapblast.com, and www.bing.com\maps. Also see U.S. Pat. Nos. 4,974,170, 5,682,525 and 6,148,260, the entirety of each of which is incorporated by reference herein.

In one embodiment, a map database 270 of server 204 stores map-related information 272, 274, 276 at least a portion of which may be transmitted to a client device 184,192. As shown in FIG. 1D, for example, map database 270 may store map tiles 272, where each tile is a map image of a particular geographic area. Depending on the resolution (e.g., whether the map is zoomed in or out), one tile may cover an entire region such as a state in relatively little detail. Another tile may cover just a few streets in high detail. The map information is not limited to any particular format. For example, the images may comprise street maps, satellite images, or a combination of these, and may be stored as vectors (particularly with respect to street maps) or bitmaps (particularly with respect to satellite images). The various map tiles are each associated with geographical locations, such that the server 204 is capable of selecting, retrieving and transmitting one or more tiles in response to receipt of a geographical location.

As noted below, the locations may be expressed in various ways including but not limited to latitude/longitude positions, street addresses, points on a map (such as when a user clicks on a map), building names, other data capable of identifying one or more geographic locations, and ranges of the foregoing.

In an embodiment, the system is configured to allow a user to associate an area with a point of interest using geofencing techniques as known in the art. Geofences can also be boundaries for areas generated for automatedly identified points of interest areas, such as frequent stop locations or Hotspots, as described herein and in U.S. patent application Ser. No. 13/632,23, Entitled: SYSTEM AND METHOD FOR AUTOMATED IDENTIFICATION OF FREQUENT STOP LOCATIONS FOR VEHICLE FLEETS, the entirety of which is incorporated by reference hereby. Accordingly, a geofence is a virtual perimeter associated with real world geographic areas. A geofence can be a predefined set of boundaries, like school attendance zones, neighborhood boundaries, or predetermined zones identified by or for a user which can be uploaded into the system. Geoefences can also be custom digitized. When a location-aware device such as a GPS device enters or exits a geofence, the system can be configured to generate a notification and/or record or log and store the entry for subsequent reporting and processing.

Custom geofencing, such as geofencing for user identified POIs, allows users of the system to either draw zones or select predefined zones around points of interest, for example, places of work, depots, offices, customer sites and secure areas. When a geofence is crossed or entered by an equipped vehicle or person with a location aware device, system can be configured to trigger a warning or alert (e.g. to a user or operator via SMS or Email) and/or record or log and store the data for reporting.

Accordingly, geofences include user defined and generated and identified boundaries for areas as represented on a map. The area can be associated with a point of interest for a user. Geofences can also be automatedly generated for points of interest such as automatedly identified frequent stop locations, or “Hotspots,” as described herein. The representations are represented on the graphic user display as polygons, squares or whatever shape is desired. Once defined, if a vehicle with the GPS device is in the geofence, as indicated by the GPS location data, the system can be configured to send and alert or record GPS event data for the geofence. For example, a user can input data indicating the geofence is for a work site, an office site, a client location or an employee's home or local bar. The fleet tracking system can then be configured to alert and report on vehicles equipped with a GPS device that a geofenced area, for example on location, when the vehicles enter or exit the geofenced area, how long the vehicle spent there, driver information, and operational metrics associated with the vehicle as described herein (e.g. engine on/off, idling, distance travelled, speed, etc).

Geofencing is useful for any number of fleet management activities, for example:

-   -   tracking arrivals and departures at job related geofence         locations (e.g. for a geofence associated with work or home,         tracking and logging when a vehicle arrives and departs from a         geofence associated with a job site, logging information for         client billing),     -   security: tracking vehicles that moves outside of a geofence         associated with specific area that it should not leave—for         example equipment yards—as well as stolen equipment recovery.

For example, as shown in FIG. 3C in one embodiment a user can select “Live Fleet” 324 using an input such as a keyboard or a mouse, which would lead to a page 325 with GPS data and mapping software which tracks vehicles v1, v2 . . . vn. The page 325 can allow a user such as a dispatcher to, for example, locate and dispatch the closest vehicle to any job site and reroute the nearest vehicle. The “Live Fleet” page 325 can be configured to show a map 329 on a graphic user interface which can be “zoomed” to higher and lower levels using interface icons 327. As a user zooms into the map 329, i.e. at a city level or closer, the graphic user interface is configured to display icons or graphics 341 a, 341 b . . . 341 n, showing user either geofences for user identified POIs or automatedly generated bounding boxes for automatedly generated frequent stop locations on the map or both. As the user zooms closer in the icons get bigger, and as the user zooms out the icons get smaller, but more Hotspots and/or POI areas can be shown due to the larger coverage of the map area.

As shown in FIG. 3D a representation 360 of an area associated with the point of interest, shown as the frequent stop location for a graphic user interface, can also include a map of the frequent stop location, including a ground level view of the frequent stop location. For example, a link or other interface input for a selected frequent stop location 340 a from the table 340 of the “Hotspots” report 350 or a frequent stop location graphic 341 a . . . n on the map from the Live Fleet page could present a user with a page or a pop-up graphic 360 including a map 361 and ground level view 362 of the frequent stop location. As will be noted, any page listing or otherwise referencing a given frequent stop location or Hotspot could be configured to link or present the frequent stop location page or pop-up 325.

The interface can also be configured with an interface input area 349 to allow a user to manually designate and save the automated frequent stop location “Hotspot” as a user designated POI in the system (i.e., independent of stop events used to identify and rank automatedly determined frequent stop locations as described herein). The graphic also includes details for reporting on the frequent stop location, including a frequent stop location address 343 a, rank 343 b, total number of visits (“# Visits”) 343 c, total number of vehicles making the stops 343 d, average stop duration 343 e, average arrival time 343 f, average travel time 343 g, average distance travelled 343 h, as well as a total stop time for the frequent stop location 343 j. As will be noted, in an embodiment these report details can be configured to show overall totals for the frequent stop location as opposed to those filtered by a time criteria in FIG. 3B, although options for reporting within filter criteria can be programmed as well.

Still other reports based on GPS data and tracking could be provided, such as driving behavior including vehicle speed, engine start-up and shut-down and idling time, or any others including as described herein which can be used to enforce driving policy and curb unwanted behavior like excessive speeding, tardiness and extended vehicle idling.

Alerts can be flagged in relevant reports, and users can also be notified of any alerts as soon as a violation occurs via email or mobile device. It will be noted that in some embodiments, a portable device such as a smart phone or PDA can be configured to receive alerts that need urgent attention, such as an off-hours use alert or an alert indicating rerouting is needed. A report can also include an alert for a frequent stop location or Hotspot. For example, as shown at FIG. 3E, a “Daily Report” for a fleet showing vehicle stops, exceptions, and a log of stops for a vehicle can be configured to show text graphics 363 a, 363 b, . . . 363 n, an orange text link for instance, by any arrival and departure stops that have been identified as a frequent stop location. As noted above, the graphic 363 a, . . . n can link to a page or present a pop-up that shows further detail about the frequent stop location as shown in FIG. 3D.

In an embodiment a computer system the GPS event database includes a frequent stop location database including stop event data associated with the vehicles. The system also includes logic for at least one criterion for identifying a frequent stop location. The GPS event data includes location information for the vehicle and stop duration and stop number information, which is stored in the GPS event database. Operational metrics for the GPS event history are derived for, inter alia, a vehicle stop event data, using for example engine on/off event at a location where an engine for a vehicle is shutdown and started up, indicating a vehicle stop. For another example, idling for a predetermined period of time at a location (e.g. a time in the range of at least 5 minutes to at least 30 minutes or more). The operational metrics can include other metrics described herein useable to identify a vehicle stop.

In one embodiment, the computer system comprises: a criterion for GPS event data used for identifying a frequent stop location. As will be appreciated, the embodiments described herein can be implemented such that a computer can automatedly and without human intervention identify frequent stop locations for fleet, referred to herein in various embodiments as “Hotspots,” based on vehicle behavior such as vehicle stop data.

Data acquisition is as follows, GPS event data is acquired and stored from GPS devices with vehicles as described herein. GPS event data for the vehicle is associated with a stop event parameter, such as engine on and engine shutoff or engine idling for a predetermined period of time. As will be understood, as more GPS data events are accumulated, accuracy and options for reporting can become more robust as well.

FIG. 4 shows a flow chart for a method and system flow for processing and presenting GPS event data for a graphic user interface, in accord with embodiments as described herein. As shown in block 402, the system receives GPS event data transmitted from a plurality of GPS devices, each GPS device being associated with a vehicle. At block 404, the system stores, for each vehicle, in a memory operatively coupled to at least one of the processors, the GPS event data over an operation period. At block 406, the GPS event data is analyzed to derive a plurality of operational metrics for each vehicle, including analyzing the GPS event data to derive vehicle stops for at least one vehicle. At block 408, the system determines if the number of stops meets a frequent stop location criterion. At block 409, the system identifies at least one frequent stop location based on the vehicle stops. At block 410, the system can provide, for a graphic user interface, a display representation of the frequent stop location for at least one vehicle associated with an entity, as for example a company or company fleet. As used herein, a fleet or vehicle fleet is understood to refer to any entity (e.g. a business entity) for whom at least one vehicle is being tracked and GPS based data is provided for, including frequent stop locations as described herein.

The method further comprises, identifying a stop location for a vehicle. Described is a method to identify a frequent stop location in accord with embodiments described herein comprises recording vehicle stops for an entity associated with the vehicle over a predetermined period of time. For example, stops are collected or processed for a predetermined time period, for example 1-6 months, which act as “seed” stops. In one embodiment the system processes stops for 3 months. As will be appreciated, any time period can be used depending on the type of entity for whom the stops are being used to identify frequent stop locations, even weekly. If the number of stops meets the threshold number of stops, the area is identified as a frequent stop location.

FIGS. 5A-5B shows a flow chart for a method flow for processing GPS event data to identify a frequent stop location in accord with embodiments of the system as described herein. FIG. 5 shows an embodiment, illustrated with respect to an initial determination of frequent stop locations taken first for an entity. At block 501, the system starts with the identified seed stop. At block 502 the system determines if a number of stops meets a threshold number of stops for an area in the time period. In one embodiment the system is configured determine the area as a function of the distance from the stop location of the vehicle. For example the system can be configured to employ a trigonometry function to determine a radial distance around the seed stop, as described below with respect to FIG. 6. The distance can be in the range of a radius of about 0.1 miles to 1.0 miles. In an embodiment, distance is in a radius of about 0.1 miles around the seed stop. As will be understood, the threshold number of vehicle stops can be any number of stops over any defined time period, depending on variables such as, for example, fleet size. For example, for a fleet of up to around 200-400 vehicles, stops can be in a range of 2 to 50 stops over a month. However for a much larger fleet, numbering in the thousands or larger, the threshold number of stops can be proportionally or similarly greater, and the time period can be shorter as well, for example, a week. In one embodiment the threshold number of vehicle stops in a range of 5-10, over a defined time period of one to three months.

For example, in making an initial determination of frequent stop locations for a fleet, the threshold number of vehicle stops can be at least 5, and the distance is in a radius of about 0.1 miles. In making an initial determination for seed stops, the system can be configured to use a lower stop number variable for the threshold frequent stop location determination. For example, while a system may be configured to use 10 stops as a threshold, as described in an embodiment below; at an initial batch the system can be configured to use a 5 stops variable for this data and unlimited frequent stop location per company (i.e., with no limitations on the number of hotspots identified). However, embodiments with a limitation on the number hotspots can be configured as well. For example, in one embodiment the stop events are calculated month by month for Hotspots, which can be extended to a previous number of months (e.g., up to 3 months) for “orphans”—stops that do not yet correlate with a Hotspot.

If the system determines that a stop does not have the threshold number of stops within the area, at block 503 the system then proceeds to the next identified stop to start another analysis for the next vehicle stop 501. If, however, the number of stops does meet the threshold, at block 504 the system saves the stop as a frequent stop location seed stop. At block 505, determines if there are any more stops to identify for the entity. If so, at block 503 the system then proceeds to the next identified stop to start another analysis for the next vehicle stop 501.

If at block 505 the system determines there are no more stop events to analyze, the system proceeds to process the frequent stop location seed stops saved at block 504 as shown in FIG. 5B. At block 507 the frequent stop location hotspots are ordered by stop count, e.g. from highest stop counts per frequent stop location to lowest stop counts per frequent stop location. At block 508, the frequent stop location with the highest number of stop counts is recorded and saved as a file for the frequent stop location.

Next, the system is configured to exclude any stops associated with an identified frequent stop location from being identified a separate frequent stop location. At block 509 the system determines if any stops in the frequent stop location are within the boundaries of a previously identified frequent stop location. If so, at block 510 the system deletes all stops from the data structure that are within the boundaries of the previously identified frequent stop location. The system flow then returns to block 507, where the system reorders the frequent stop locations to run the process again. The system continues the process until it determines there are no further frequent stop locations for the fleet above the required threshold of stops, and further, attributes the stops to each frequent stop location without overlap. The system is thereby configured to run the process to determine all the frequent stop locations for the data structure and create and record frequent stop location files therefor.

FIG. 6 shows a flow chart for a method and system flow for to implement a trigonometry function to determine a radial distance around the seed stop, as described above. At block 601 the system identifies a stop derived from the GPS data and gets the location data for the stop, for instance, a latitude and longitude for the stop. As noted above, an on-board GPS device can be configured to be operational to transmit when a vehicle engine is on, thus engine on/off time can be derived. Such events can be stored over time in a database (see, for example FIGS. 14A-14C) and arrival (shutoff) and departure (engine on) times can be logged. At block 602, the system is then configured to take the position for the location and define the area for the frequent stop location. At block 603, the system identifies the furthest outside stops in the remaining area; and defines a boundary shape for the frequent stop location using the furthest outside stops. For example, the system is configured to take the furthest stops, for example (e.g., the outermost stops) in the north, south, east, and west, to define a square boundary. At block 604 the system determines number of stops in the area meets the threshold number of stops for the area. At block 605, the system associates and saves vehicle stop information with the identified frequent stop location, for example location information such as an address. The vehicle stop information can also include a time and an entity identifier, as described herein.

For example, in one embodiment the threshold number of vehicle stops is 10, and the distance is in a radius of about 0.1 miles. Each stop is compared to a 0.1 mile radius to see if there were 10 stops in the vicinity. If there are 10 stops, then the area is identified as a frequent stop location, shown for example as a “Hotspot” in illustrative embodiments herein. The system is then configured to define a geofence for the area of the frequent stop location, by taking latitude and longitude (0.1) and remove anything that is either side of it. For example, with a lat/long of 75.234, the system removes 75.134 and 75.334. The system is configured to take the furthest stops, for example the outermost stops in the north, south, east, and west as noted above, to define a polygonal boundary, for example a square or rectilinear boundary. The system is also further configured to record and save the boundaries, a company ID and a Hotspot name, as well as a time stamp. The Hotspot name can be, for example, an address plus the Hotspot ID and Time Stamp (when it was created). A total stop count for the Hotspot is also recorded in a database. The total stop count can power a ranking as described herein. The frequent stop location with the highest number of stop counts can be saved as a record in a frequent stop location database for the frequent stop location, for example in an “HSCreate” folder.

As noted above, the system can be configured to rank a plurality of frequent stop locations. The system is configured to determine a total number of recorded vehicle stops for each of the plurality of frequent stop locations; and ranking the frequent stop locations by the total number of recorded stops in each frequent stop location.

The address can comprise, for example, a relevant or targeted address that is relevant to the frequent stop location. An identified frequent stop location may have a number of addresses associated with it. In one embodiment, the system is configured to determine if there is an address for each latitude/longitude identified as a frequent stop location. For example, the system may employ a reverse geocode, which is a process of back (reverse) coding of a point location (latitude, longitude) to a readable address or place name, as known in the art. For each frequent stop location, the system performs a comparison to see which address is determined the most. The system can then be configured to use the address for the Hotspot name. In another embodiment, the system is configured to identify the most central prevalent address. For example by series of stop points in a bounding box for a frequent stop location as described herein, the system can be configured to obtain the average lat/long—which for a grouping of points may not be a center point for the box. For a concentration of points in an area of the box, an average lat/long it taken and the stop point closest to that average lat/long point is identified as the most relevant address for the frequent stop location. This stop can be identified as a prevalent stop in addition to the identified stop's latitude/longitude. In one embodiment, the record stored in a frequent stop location database can have a format configured in accord with the frequent stop location information, for example “[Hostpot pk_id, 4 corners (min and max lat/long), timestamp, most prevalent address, and relevant lat/long],” In one embodiment, if a series of stop points in a bounding box for a frequent stop location are too close to each other, for example with 15 stops but they all have the exact same latitude/longitude, which can appear as a small dot when displayed on a map display, the system can be configured to generate bounding box around the smaller “dot” to visually display the bounding box, for example on that is 10 meters by 10 meters around lat/long.

In an embodiment shown in FIG. 7, the system is configured for updating frequent stop locations for an entity such as a fleet. After one or more frequent stop locations for a fleet have been identified, the system can record vehicle stops for a period of time subsequent to identifying and establishing frequent stop locations and updating the frequent stop locations with the subsequently recorded stops. As shown at block 701, the system inserts a new frequent stop location file of subsequently recorded stops to a frequent stop location database. The system is also configured to determine whether one or more subsequently recorded vehicle stops are associated with an existing frequent stop location; or determining if the subsequently recorded vehicle stop(s) meet a criterion for a new frequent stop location. At block 702, the system determines whether any subsequently recorded vehicle stops are associated with an existing frequent stop location by comparing information about the subsequently recorded vehicle stops against saved frequent stop location identification information. For example the new file can be compared against an existing frequent stop location the created or saved from a prior time period, as described above. The system can compare, for example, frequent stop location identification information including any or all of: a location associated with the frequent stop location; an address associated with the frequent stop location a name associated with the frequent stop location; or a total number of stops for a frequent stop location. At block 703 the system determines if at least one existing frequent stop location matches the new stop at least one existing frequent stop location matches any stops from the new stops, at block 710 the system updates the existing frequent stop location(s) with the new information, such as adding any stops to the total stop count for respective frequent stop locations.

If there is not an existing frequent stop location, at block 706 the system determines if the stop meets a frequent stop location threshold criterion for identifying a frequent stop location as described herein (for instance as shown at FIG. 6). For example, the system may determine that a new, non-overlapping area that previously had no stops now has over 15 stops in a 0.1 radius and geofences boundaries derived therefrom, as described herein, where the threshold criterion for stops is 10 or more. In such case at block 709 the system identifies and saves this as a new frequent stop location for the fleet. If, however, the number of stops does not meet the threshold at block 708, the process ends, although as noted below, the in certain embodiments the stops may be saved and rolled in to a subsequent update or predetermined number of updates over subsequent time periods to determine if the area should be identified as a new frequent stop location.

The updating process can be repeated continually, for example on a periodic basis. Accordingly, frequent stop locations can be updated, for example, every month, 2 months, every 3 months (e.g., every quarter), or even every week. In one embodiment, the periods or any other choice of time for updating can be customized or adjusted to fit a given entity's needs. In another embodiment, the system is configured to offer updates on a fixed basis, for example, a period in the range of every 1-6 months (e.g., every 3 months or every month). For example in one embodiment the system updates on a fixed, non-overlapping basis every month to determine if there are any new frequent stop locations, and does not save any stop locations that do not meet the threshold at each update.

In an embodiment, the system is configured to update at different periods over time. This embodiment can be implemented a “rolling” system that updates for a number of time periods (e.g., every month for 3 months) saving a record of seed stops from the analysis of the prior time period that did not meet the threshold. For example after an initial determination of frequent stop locations, the system updates every month for 3 months on a rolling basis, and then on a fixed, non-rolling basis every 3 months thereafter. The system rolls these stops over the number of time periods (e.g., 3 months), whereupon if in that given number of time periods the number of recorded stops meets or exceeds the threshold, then the area is saved as a frequent stop location.

As will be appreciated, the above-noted examples of fixed and rolling updates can be configured in any number of ways depending on, for example, the needs of the fleet or combinations or other implementations as may be useful (e.g., continually rolling updates, rolling updates at certain time periods and fixed updates at others, or fixed updates with no rolling or overlapping stops). For example “Hotspot” generation performed as a “Weekly Hotspot generation” where the system is configured to checks 30 days-back data on a weekly basis.

Geofence Categorization

In an embodiment disclosed is a computer system and methods therefor, the computer system including at least one computer processor and computer readable storage medium or media including computer code and at least one storage device in which is stored GPS data for at least one vehicle the system comprising: a memory including a GPS event database including GPS event data transmitted from at least one GPS device, each GPS device associated with a vehicle, and stored over a period of time; wherein the one or more processors programmed at least to receive GPS event data transmitted from at least one GPS device, each GPS device associated with a vehicle, the event GPS event data including location information for the vehicle; store the GPS event data in a GPS event database operatively coupled to at least one of the processors; analyze the GPS event data to determine vehicle stops for at least one vehicle; determine if the vehicle stops meets a location type identification criterion for a geofence; and if the criterion is met, automatedly categorize the geofence as a location type. As will be appreciated, the location information be provided or supplemented by other sources of location tracking data as known in the art, such as wireless transmission of location data via wireless networks (LAN, WLAN) where GPS signals are weak or unavailable.

In an embodiment, the system is configured to automatedly categorize the location type of the geofence, the location type comprising a location type being selected from the group of: an office location type, a depot location type, and a home location type. The geofences can include a boundary shape for at least one of a boundary shape for an automatedly identified frequent stop location (“Hotspot”), a boundary shape for a user-identified point of interest, and/or boundary shape for predetermined point of interest. The system is configured to automatedly categorize the location type if one or more criteria are met, for example as an office location type, a depot location type, or a home location type.

In an embodiment the system is configured to automatedly categorize the location type by determining vehicle stop characteristics for the geofence; and determining if the vehicle stop characteristics for the geofence meet an office or depot location type identification criterion.

For example in an embodiment shown in FIG. 8, as shown at block 802, the system is programmed to determine the vehicle stop characteristics by at least: identifying a total number of stops for at least one fleet of vehicles. In the embodiment described hereby, the geofences are automatedly identified frequent stop locations, or “Hotspots.” However it will be appreciated that the system can be configured to work with other forms of geofencing, including user identified or uploaded geofencing or other geofencing techniques known in the art. An exemplary Hotspot count is shown at Table 1, further described herein.

At block 804, the system identifies and sorts the stops in order of the geofences comprising the highest average percentage of stops of the total stops for each geofence. In an embodiment the identification of a total number of stops for at least one fleet of vehicles is identified and sorted by a predefined geographical region, for example by state, county, country, a user defined region or any other defined region. Table 2 shows an example of Hotspot averages, further described herein.

At block 806, the system identifies one or more of the geofences having the highest average percentage of stops as the highest ranked geofences. For example, in one embodiment, the system can configured to identify 3 to 50 geofences comprising the highest average percentage of stops as the highest ranked geofences. In another embodiment, the system may identify 5 to 25 geofences comprising the highest average percentage of stops as the highest ranked geofences for processing. In another embodiment, the system may identify 5 to 25 geofences 7 to 10 geofences comprising the highest average percentage of stops. In a particular embodiment, as shown in Tables 1-4 and described herein, the system is configured to identify 7 geofences, shown as the top 7 Hotspots for a plurality of fleet accounts comprising the highest average percentage of stops.

At block 808, the system determines the variability of the stops for each of the highest ranked geofences. For example, as shown at Table 3 (“Stop Centrality”) determination of variability comprises calculating the standard deviation across the identified highest ranked geofences.

At block 810, the system identifies, for each of the highest ranked geofences, the number of stops that are last stop for a vehicle for that vehicle for a time period.

At block 812, the system identifies, for each of the highest ranked geofences, a unique vehicle count for the stops.

The system is configured to determine if the vehicle stop characteristics for the geofence as determined in blocks 802 to 812 meet a location type identification criterion. At block 814, the system selects one or more of the identified highest ranked geofences for categorization, and at block 816, the system determines if the one or more selected highest ranked geofences meet one or more location type identification criteria. At block 818 the system categorizes one or more of the selected highest ranked geofences as a location type if the one or more selected highest ranked geofences meet a threshold number of the location type identification criteria In an embodiment, the criteria includes:

(1) A threshold coefficient of variation criterion. For example, to categorize a location as an office location type, the number of geofences identified as having the highest average of stops is 7, and the threshold coefficient of variation can be at least 0.7 (e.g. 4/7). A high variability of stops indicates that for a fleet, a top ranked geofence is likely an office, whereas a low variability for the stops across ranks indicates it is difficult to distinguish if any one or more of the geofences is or is not an office. Thus to categorize a location as for example, a depot location type or secondary office type, where the number of geofences identified as having the highest average of stops is 7, an acceptable threshold coefficient of variation may be at least about 0.3 to about 0.5.

(2) A minimum percentage of the stops in the geofence of the total stops for the number of stops identified for the geographic region or fleet. For example, in order to be classified as an office, the system can be configured to require that the average number of stops at the geofence is at least 10% of the total stops for the fleet or a geographic region for the fleet.

(3) The number of stops for the geofence is a minimum number of stops for a predetermined time period. For example, the system can be configured to require that for a geofence, there be least 2000 stops for retained business of a fleet entity for 1 year or a minimum of 500 stops for 3 months for a new business for a fleet entity, whereby the geofence is identified as an office location type if the criteria is met. In another embodiment the minimum number of stops can be 1500 for retained business for 1 year retained, and a minimum number of 200 stops for 3 months for a new business for a fleet entity, whereby the geofence is identified as a depot location type or secondary office type.

(4) At least one of the stops for the geofence is the last stop of the day for a vehicle (vehiclelast_stopOfDay_count—great than 0).

(5) A criterion requiring that a threshold number of stops are stops per unique vehicles, for example at least 30 stops per unique vehicle.

As noted above, the criteria above are exemplary, and other criteria can be employed as filters and rules. For example, the location type criterion can comprise an industry based criterion, such as a filter that excludes a “depot” identification for truck stops for a company engaged in long range delivery (e.g. over 300 miles).

In an embodiment wherein the vehicle stop characteristics for the geofence must meet a threshold number of the location type identification criteria, the threshold for the number of criteria can be adjusted depending on the degree of certainty desired for the system, ranging from one or two to all criteria for a system. For example, in an embodiment with 5 criteria, the threshold number location type identification criteria may required that a range of anywhere from 2 out of 5 criteria to 5 out of 5 criteria be met for an geofence before an automated location type categorization is made. If a high degree of certainty is desired, the system may be configured not to categorize the location type unless 4 out of 5 or 5 out of 5 criteria are met. For example, from the exemplar data of Tables 1-4, only the first ranked Hotspot would be selected for categorization where all criteria for a candidate hotspot must be met (4 out of 4 or 5 out of 5). If a lower degree of certainty is desired, 2 or 3 criteria may be sufficient for a categorization. Also, as will be appreciated, different criteria can be weighted differently, and there can be any number of criteria for a system.

In another embodiment, the system is configured to at least select a number of ranked geofences for categorization. For example, in one embodiment, the system is configured to select only a first highest ranked geofence, whereby the first highest ranked is categorized as an office location type. In another embodiment, the system is configured to identify one or more of the highest ranked geofences having the highest number of stops for categorization, whereby the system is configured to at least select a first highest ranked geofence to a second highest ranked geofence, whereby the first highest ranked geofence is categorized as an office location type and the second highest ranked geofence is categorized and a depot location type or an office location type. In another embodiment, the system can be further configured to select a third highest ranked geofence to a sixth highest ranked geofence, whereby the third highest ranked geofence to the sixth highest ranked geofences are categorized as depot location types. In another embodiment, the system can be further configured to select a select third highest ranked geofence to the fifth highest ranked geofence, and the third highest ranked to the fifth highest ranked geofences are categorized as depot location types.

Factors and Calculations

Automatically identifying a geofence as an Office type location or Depot type location can be configured to be at the “account” level for a system, that is, for an account for an entity associated with a fleet. An office or depot geofence is a location node characterized by its “centrality,” which is characterized by a large number of fleet vehicles that travel to and from this node.

Scoring the role of certain account stops as “Offices/Depots” can rely upon a network analysis of nodes or hubs that measure stop “centrality,” that is, “centrality degree” and “betweenness centrality.” A stop's “degree centrality” can be characterized as the number of stops that are connected to that stop, that is, stops which are the most active in the sense that they have the most ties to other stops in the network, “Betweenness centrality” focuses on the share of times that a stop [stop] i needs a stop [stop] k (whose centrality is being measured) in order to reach j via the shortest path. That is to say, the more times a stop lies on the shortest path between two other stops, the more control that the stop (Office/Depot) has over the interaction between these two non-adjacent/non-(direct) related stops.

As described herein, the scoring scenario refines the calculation of stops' “degree centrality” as opposed to “betweenness centrality” since the latter may be heavily influenced by a particular account's vehicle routing decisions.

Factors and calculations in identifying “Office/Depot” (O/D) hub categorization of certain Hotspots can be at an account level. Factors used area

UVCFleet—Count of total UNIQUE vehicles count for an account (fleet size)

USCFleet—Count of total UNIQUE stops for account

UVCstop—Count of total UNIQUE vehicles count for each Hotspot USCstop—Count of total UNIQUE stops connecting to Hotspot

SCFleet—Count of total stops for account SCstop—Count of total stops at Hotspot.

Scoring calculations employed are:

Normalized UNIQUE stops degree centrality=USCstop/(USCFleet−1)

UNIQUE stops degree centrality scores for the number of stops/Hotspots attached to that stop.

Normalized ALL stops degree centrality=SCstop/(SCFleet−SCstop) All stops degree centrality scores for the number of stops that flow into (attached) to that stop.

Normalized UNIQUE vehicles degree centrality=UVCstop/(UVCFleet−UVCstop) UNIQUE stops degree centrality scores for the top Hotspot with most vehicles that travel to/from it.

Reference Hotspot comprising Hotspot with maximum number of stops (# stops, total unique vehicles, # active days, count of vehicle last stop of day)

Other considerations are that a candidate “Office/Depot” (O/D) huh NOT be: an Employee Home, a Gas Station, ora Shopping Mall

As described herein, embodiments score threshold definitions that characterize “Office/Depot” (O/D) hub, the results of which were validated as described herein.

EXAMPLES

To understand the relationships between various descriptive variables of Hotspots, the (correlation) analysis below was done on a Group 1 dataset of all accounts' top 7 Hotspots by stops) and a Group 2 dataset of all Hotspots for 12 selected (12) accounts. The analysis enabled the determination of variables to define the location types of points of interests associated with a geofence, here shown as frequent stop locations (Hotspots), that are Offices or Depots—Hotspots that are meaningful central hubs of account activity.

An Office identification is an account owned hub/“headquarters”/Office place of doing business. A Depot is regarded as an auxiliary location for example, account owned or non-owned/not clients, where vehicles frequently stop, for example pick up inventory or make delivery services frequently, etc., but are not a regular hub/“headquarters”/Office.

The Tables 1-4 and Chart 1 (for Group 1) and Chart 2 (for Group below show that descriptors of hub activity are:

-   -   Total stops at Hotspot (SCStop_total stops or totaiStopsHS);     -   Active days at hotspot (activeDays);     -   Unique vehicles stops at Hotspot (UVCStop_uni_vehicles); and     -   Last stop of Day count at Hotspot;

One exemplary advantage of the embodiments described herein is that they control for the fact that smaller frequent stop locations can have the same high centrality score as a very busy central hub (Office).

From the summary statistics in the Group 1 Tables 14 and Chart 1, it is shown that 90% of top frequent stop locations pass 3-4 of the “Office” logic filters at account/state level (bold cells in Group 1 Tables 14), have a high stop centrality (1.1) and average stops per Hotspot (930).

Hotspots that are top ranked but pass only a few (0-2) of the “Office” logic filters may indicate that there are no offices in the state.

The next tier of “Depot” Hotspots (italicized cells) indicate an elevated but relatively much smaller stop centrality and many fewer stops per Hotspot.

The “Depot” Hotspot designation refers to any secondary Offices or Depots in the state for an account and, if so, how many.

Group 1

TABLE 1 Hotspots Count Nbr. of Rank of Hotspot for Account/State Filters Grand Passed 1 2 3 4 5 6 7 Total 4 10,834 4,361 1,533 338 25 1 17,092 3 2,484 5,554 5,010 4,002 2,787 2,206 1,864 23,907 2 1,138 3,761 5,502 5,746 5,395 4,719 4,078 30,339 1 399 1,154 2,744 4,568 6,104 6,924 7,536 29,429 0 15 36 77 212 555 1,015 1,377 3,287 Grand Total 14,870 14,866 14,856 14,866 14,866 14,865 14,855 104,054

TABLE 2 Hotspots Mix Nbr. of Rank of Hotspot for Account/State Filters Grand Passed 1 2 3 4 5 6 7 Total 4 73%  29% 10%  2%  0%  0%  0% 16% 3 17%  37% 34% 27% 19% 15% 13% 23% 2 8% 25% 37% 39% 35% 32% 27% 29% 1 3%  8% 18% 31% 41% 47% 51% 28% 0 0%  0%  1%  1%  4%  7%  9%  3% Grand Total 100%  100%  100%  100%  100%  100%  100%  100% 

TABLE 3 Stop Centrality Nbr. of Rank of Hotspot for Account/State Filters Grand Passed 1 2 3 4 5 6 7 Total 4 1.10 0.21 0.14 0.11 0.10 0.09 0.77 3 0.39 0.14 0.09 0.06 0.04 0.03 0.03 0.11 2 0.26 0.11 0.08 0.06 0.05 0.04 0.03 0.07 1 0.24 0.12 0.08 0.06 0.05 0.04 0.03 0.05 0 0.05 0.06 0.07 0.07 0.07 0.05 0.05 0.06 Grand Total 0.89 0.15 0.09 0.06 0.05 0.04 0.03 0.19

TABLE 4 Average Stop Count per Hotspot Nbr. of Rank of Hotspot for Account/State Filters Grand Passed 1 2 3 4 5 6 7 Total 4 830 330 220 170 137 70 634 3 338 199 148 127 111 105 99 164 2 161 141 114 100 92 82 76 103 1 64 51 47 39 35 31 28 35 0 40 37 34 31 28 24 21 24 Grand Total 676 211 124 89 70 58 49 182

CHART 1 Last Total HS Total Stop Avg Stops Active Stop Stops Day Time of Avg Stop TYPE NAME Intercept HS Days Centrality State Count Stop Duration MEAN 1 611.74 62.49 0.88 932.66 128.31 46167.72 35.91 STD 0 860.34 20.58 1.22 3796.51 217.86 7474.58 987.71 CORR Total . 0.42 0.13 0.77 0.68 −0.06 0 Stops HS CORR Active Days . 0.42 0.2 0.28 0.36 0.06 0.01 CORR HS Stop . 0.13 0.2 −0.09 0.23 0.1 0 Centrality CORR Total Stops . 0.77 0.28 −0.09 0.46 −0.09 0 State CORR Last Stop . 0.68 0.36 0.23 0.46 0.21 0.01 Day Count CORR Avg Time . −0.06 0.06 0.1 −0.09 0.21 0.02 of Stop CORR Avg Stop . 0 0.01 0 0 0.01 0.02 Duration

Group 2

Last UNI Total SC Stop stop of Avg Vehicle Stops Active UVCStop_(—) total USC Day Time of Avg Stop _TYPE _NAME_(—) Intercept Centrality HS Days uni_vehicles stops Stop count Stop Duration MEAN 1 1.05 0 209.63 43.26 2144.51 8.15 391 41480.71 3.94 STD 0 4.29 0 136.26 44.7 5062.51 7.42 1584 7405.39 24.85 N 300 300 300 300 300 300 300 300 300 300 CORR UNI Vehicle 0.1 0.17 0.03 0.18 0.25 0.12 0.01 0.05 Centrality CORR UNI Stop 0.1 −0.25 −0.22 −0.09 0.17 −0.06 0.25 −0.02 Centrality CORR Active Days 0.17 −0.25 0.5 0.55 0.4 0.37 0.02 0.16 CORR UVC 0.03 −0.22 0.5 0.71 0.29 0.67 0.14 0.08 Stop_uni_(—) Vehicles CORR SC 0.18 −0.09 0.55 0.71 0.29 0.85 0.19 0.14 Stop_total_(—) stops CORR USC Stop 0.25 0.17 0.4 0.29 0.29 0.18 0.05 0.01 CORR Last Stop of 0.12 −0.06 0.37 0.67 0.85 0.18 0.24 0.1 Day Count CORR Avg Time 0.01 −0.25 0.02 0.14 0.19 0.05 0.24 0 of Stop CORR Avg Stop 0.05 −0.02 0.16 0.08 0.14 0.01 0.1 0 Duration

Office Intersect with Gas Stations

It was also determined that most likely candidates for Office (top ranked in state by acct, that pass 3 or 4 filters) have the lowest proportion of gas stations (2%).

The analysis is different for the minor offices or depots in state which can have up to 13% gas stations for 2^(nd) highest ranked HS. This is because gas stations are not as much a hub for activity (as Offices) but are still very high ranked if popular. Gas stations can be filtered by a requirement that at least one of the stops over a time period be a last stop of the day for a time period.

“Office” Algorithm Validation

Validation was done through Hotspot lat/long Google Maps search to identify a Hotspot location for a company's office by either looking at the business name (via a Google Places label) and/or doing a Google text search and/or going to the company website to validate their office locations, as well as other tools. In some instances, account owners were contacted to confirm results when not 100% sure from the Maps search etc. (“Maybe”) or thought that there definitely was no Office (“No”).

Results

The automated identification of office type locations for Hotspots were found to have a 94% successful match rate confirmed results—140 “Yes”, 8 “Maybe” and 10 “No” out of 158 Total Office candidate Hotspots.

Also, it was found that for an “Office” vs. Gas Stations intersect, most likely candidates for Office (top ranked in state by account, that pass 3 or 4 filters) had the lowest proportion of gas stations (2%) which serves as another independent validation of the segmentation.

Categorization of Home Location Types

In an embodiment, the system is configured to determine if a geofence meets a home location type identification criterion for the geofence. For example in an embodiment shown in FIG. 9, as shown at block 904, the system is programmed to identify at least one geofence associated with a single vehicle. At block 904, the system is configured to determine, for each geofence and associated with a single vehicle, a number of stops for the geofence, wherein the number of stops over a time period are the last stop of the day for a predetermined time period. For example, in one embodiment the predetermined time period is a 60-day period, however other time periods can be employed, for example 30 days, a month, a quarter, a year, and so on. At block 906, the system is configured to determine a duration of time for each of the number of stops over a time period are that are the last stop of the day. In one embodiment, the minimum duration of the stops that are the last stop of the day is 5 hours or less. The five hour duration is adopted to count vehicle stops where a second or third shift occurs across consecutive days, as such counts could be prevented if greater durations are permitted. As will be appreciated, the duration can be adjusted, for example by plus or minus one or two hours.

These vehicle stop characteristics being identified, the system is configured to determine if the geofence if location type criterion comprising criteria including: a minimum number of vehicle stops in the geofence that are the last stop of the day for the single vehicle (block 908); a minimum number of vehicle stops in the area are the last stop of the day; (block 910); determining a duration of time for each of the number of stops over a time period are that are the last stop of the day, and an address designation for a minimum percentage of stops within the geofence is designated as a qualified home address (block 912), for example by checking the vehicle stop location data against an external database of addresses (e.g. Google Places).

As noted above, at block 908 it is determined if wherein the minimum number of vehicle stops in the geofence determined to be the last stop of the day for the single vehicle. In one embodiment, the minimum number is at least twenty. This minimum number is selected to avoid a first movement of the day plot filter problem identified in the analysis below.

In another embodiment, the system can be configured to determine if fleet policy information indicates an entity associated with a fleet permits a home location for a vehicle. For example, an entity associated with a fleet account may input data indicating that its employees are permitted to take vehicles home, or input data on the time for employee shifts and hours. If so, filters or criteria can be configured into the system accordingly, for example, if a fleet entity does not permit its employees to take vehicles home, adjusting the weights on the criterion for last stop of the day or filter out that account's vehicle stops entirely.

Analysis

An analysis was employed to identify the frequency of the same garaging location/address for all vehicles and use this indicator as a possible tool to identify location types for a geofence. A “Garaging Address” is a constant reference point in a vehicle's route since almost all vehicles start the day and are garaged in the same address/location—which why they are often tagged as geofences for a user defined point of interest: e.g., Employee Home or Company Office/Depot.

Much mileage and fuel cost is spent on the first (and last) leg of the daily journey getting to the first work order or getting to the Office. Among other advantages, automated identification of a Garaging Address can save time as compared to having to manually designate the location, and also allows for enforcement of vehicle use policy where the dispatcher assumes vehicle leaves each day from an “Employee Home” geofence, as well as allows calculation of cost of having employees highly dispersed relative to an Office location or regular service area.

In the analysis, the “Start” location address of a first travel and stop journey of each day for each vehicle was selected from a two month dataset derived from GPS event data, An SAS function COMPRESS, COMGED AND COMPLEV were used to compare the similarity between the address strings and identify which address represents the most frequent garaging address for each vehicle. These functions remove most variability in an address string to allow for a fuzzy match even when the addresses are slightly different but represent the same location (i.e., 215 Main Street vs. 210-230 Main St),

Geofence Types

Out of 1748 “GEOFENCE CATEGORY TYPES” identified in the dataset, 289 were identified as “Employee Home” (various labels) and 101 as “Company Office (various labels),” 22% of total GEOFENCE CATEGORY TYPES. The Employee Home and Company Office GEOFENCE CATEGORY TYPES were used as binary indicators (1/0) if a geofence identification belonging to these respective categories was indicated on a potential Garaging address. The existence of an Employee Home or Company Office designation was regarded as an additional validation that the garaging address is a viable geofence indicator.

Results

The expected result was that the data would show a high percentage of daily Start Location addresses being the same. The actual surprising result was that a large number of vehicles do not start at the same address. Referring to FIG. 10 and Table 5, it was found that 50%+ of vehicles start the day from the same address Garaging Location less than 50% of the time (see FIG. 10 shaded area 1000). The average frequency of garaging at the same location is 60% but the coefficient of variation (a normalized measure of dispersion) was 50% which is illustrated in the graph shown in FIG. 10.

Thus, contrary to expectation that the majority of vehicles have around 70%-90% the same Garaging Location, the data shows significantly more dispersion.

TABLE 5 Percentage of Total (2 mos) Daily First Start Locations That are SAME Vehicle Pct Cumulative Garaging Address for Each Vehicle Count Mix Pct. 10% 1446  3%  3% 20% 5158  9% 11% 30% 7993 14% 25% 40% 8137 14% 39% 50% 7625 13% 53% 60% 6044 10% 63% 70% 5156  9% 72% 80% 5461  9% 82% 90% 5836 10% 92% 100%  4778  8% 100% 

The data below indicates that the program for identifying most frequent Garaging Address works, having a high correlation with Geofence stops—capturing most of the geofence stops that are the 1^(st) daily stop.

There is also a high correlation with the number of days in the sample, with the average being 20 days per vehicle over the two month period, with a vehicle sample of around 30 days delivering the best results for Garaging Address determination. The sample size calculation also indicates 20 days of active days as being statistically significant—so a 60 day dataset is robust (given the actual average 20 active vehicle days) for about 50% of the vehicles.

Employee Home Customer Validation

In order to validate the efficiency of the home categorization location type algorithm, called the “Employee Home Hotspot” or “EH Hotspot” algorithm below, lists of employee home addresses for employees having a substantial number of “Employee Home Hotspots” were provided from 6 accounts with fleets of different sizes, from various, industries and various geographies. The addresses provided had a 92% successful match rate to the EH Hotspot using an address database (Google Addresses).

The labels in Table 6 below are as follows:

In the “Matches” category the employee addresses matched to the EH Hotspot address (in general, the most frequent HS address); addresses were not matched to addresses with PO boxes.

For “possible employee move” category the EH hotspot was very close to the employee submitted address.

In some instances where the employee misspelled e address, a match was made nonetheless.

In an embodiment, larger or more dense geofences or areas may be associated with multiple points of interest (for example “gigaspots” or hotspots of hotspots), can employed to help exclude false home location type categorizations created, for example, because an employee worked outside the normal “home” geofence (e.g. doing work on tornado damage out of state, etc).

TABLE 6 SUMMARY Total Total Addresses Submitted 190 PO Boxes 8 4% Total Without PO Box 182 Success Rate 92% Matches 153 Possible Employee Move See Map 12 4% Customer Misspell 2 NO MATCH at all 16 Fleet Entity Account 1 Total 21 PO Boxes 0 0% Total Without PO Box 21 Success Rate 95% Matches 20 95%  Possible Employee Move See Map 0 0% Customer Misspell 0 0% No MATCH at All 1 5% Fleet Entity Account 2 Total 62 PO Boxes 0 0% Total Without PO Box 62 Success Rate 89% Matches 52 84%  Possible Employee Move See Map 3 5% Customer Misspell 0 0% No MATCH at All 7 11%  Fleet Entity Account 3 Total 8 PO Boxes 0 0% Total Without PO Box 8 Success Rate 88% Matches 7 88%  Possible Employee Move See Map 0 0% Customer Misspell 0 0% No MATCH at All 1 13%  Fleet Entity Account 4 Total 41 PO Boxes 3 7% Total Without PO Box 38 Success Rate 92% Matches 30 79%  Possible Employee Move See Map 4 11%  Customer Misspell 1 3% No MATCH at All 3 8% Fleet Entity Account 5 Total 42 PO Boxes 5 12%  Total Without PO Box 37 Success Rate 95% Matches 30 81%  Possible Employee Move See Map 4 11%  Customer Misspell 1 3% No MATCH at All 3 8% Fleet Entity Account 6 Total 16 PO Boxes 0 0% Total Without PO Box 16 Success Rate 94% Matches 14 88%  Possible Employee Move See Map 1 6% Customer Misspell 0% No MATCH at All 1 6%

Correcting Geofences: Hotspot Geofences and User-Defined Geofences Interaction

Systems and methods for location based services can be configured to allow users to mass upload predefined geofences. Systems are also configured to allow users to manually define geofences, for example by using default geofence sizes (small, medium, large) for a selected reference address, such as user identified point of interest. User identified geofences (e.g. mass uploaded or user defined) can result in geofences with boundaries that overlap significantly, or with boundary edges that are far removed from the reference geofence address.

Geofences are employed to categorize or associate a vehicle stop with a specific reference address or POI. Accordingly, described herein are of embodiments a system configured to use geofences for automatedly identified frequent stop locations, or referred to herein as “Hotspot geofences,” as described herein and in U.S. patent application Ser. No. 13/632,23, Entitled: SYSTEM AND METHOD FOR AUTOMATED IDENTIFICATION OF FREQUENT STOP LOCATIONS FOR VEHICLE FLEETS, the entirety of which is incorporated by reference hereby, to correct the size and shape of user identified geofences to match the actual vehicle activity range at that POI.

Accordingly described are embodiments for computer system and methods therfor, the computer system including at least one computer processor and computer readable storage medium or media including computer code and at least one storage device in which is stored GPS data for at least one vehicle the system comprising a memory, the one or more processors programmed at least to: automatedly identify a conflict between a plurality geofences; apply a geofence correction algorithm; and correct the geofence. The computer system of can comprise: a memory including: a GPS event database including GPS event data transmitted from at least one GPS device, each GPS device associated with a vehicle, and stored over a period of time; and the one or more processors are programmed at least to receive GPS event data transmitted from at least one GPS device, each GPS device associated with a vehicle, the event GPS event data including location information for the vehicle; store the GPS event data in a GPS event database operatively coupled to at least one of the processors; analyze the GPS event data to derive and record vehicle stops for at least one vehicle, automatedly identify and define a geofence for at least one frequent stop location based on the vehicle stops; determine if the frequent stop location area conflicts with a user defined geofence; and if the geofence for the frequent stop location conflicts with the user defined geofence, correct the geofence.

In an embodiment, the one or more processors are further programmed at least to: provide, for a graphic user interface, a representation including a representation of the frequent stop location area, and a representation including a representation of the geofence, wherein the graphic user interface shows the overlap between the representation of the frequent stop location geofence and the user defined geofence. In an embodiment the one or more processors are further programmed at least to provide, for the graphic user interface, at least: a map showing the geofence for the frequent stop location area and the user defined geofence.

In an embodiment, the one or more processors are further programmed at least to allow a user to manually correct at least one of the geofences. In an embodiment the one or more processors are further programmed at least to: provide, for a graphic user interface, a representation including a representation the plurality of geofences, wherein the graphic user interface shows the conflict for the plurality of geofences. In an embodiment, the one or more processors are further programmed at least to provide, for the graphic user interface, at least: a map showing the plurality of geofences.

Analysis

It was determined that geofences for automatedly identified frequent stop locations (“HS”) conflicted with roughly 33% of all user-identified geofences (“GF”). In this group, around 80% of GF have an area overlap of 20% or less, as shown in FIG. 11A. Also, as shown in FIG. 11B, around 50% of HS have 50% or less of their stops in the GF.

There is only around 0.25 correlation between HS % of stops in GF vs. GF area overlap with HS—therefore, expanding this segment of GF to include HS will really improve GF coverage.

Automatic Correction Scenarios

FIGS. 12A-12C show an example of an embodiment of interactions between user identified geofences and geofences for automated identified frequently stop locations (“Hotspots”). Shown in FIG. 12A is a map display 10 graphic user interface including a “Before” view of a first user-identified geofences GF-11 that overlaps the geofence for a Hotspot HS-11. The geofences for Hotspot HS-11 also overlaps a second user-identified geofence GF-12. A popup graphic 1201, 1202, 1203 for each geofence GF-11, GF12, HS-11 includes information about the geofence, including a company ID, a name for the location, and latitude/longitude coordinates for the boundaries of the polygon for the geofences. In the case of the geofence for the Hotspot HS-11, it additionally includes information such as a reference address, a vehicle stop count, ranking, and number of vehicles visited.

As shown in FIG. 12B in a first correction scenario, the system is configured to correct the first user-identified geofence GF-12 using Hotspot geofence HS-11, whereby the geofence GF-12 expands to include and match the Hotspot area to form a corrected geofence GF-11.

In another embodiment, shown in FIG. 12C, the correction can be further refined by resizing the second user defined geofence GF-12, which is associated with a different point of interest, such that the upper boundaries of GF-12 shorten so as not to overlap the hotspot corrected geofence GFH-11, thus better differentiating the points of interest or reference addresses associated with the respective geofences.

As shown above and in FIGS. 13A-13K the system is configured to identify an overlap between a user-identified geofence and a Hotspot and present the overlap or other interaction on a map showing the overlap of the boundaries of the user-identified geofence and the Hotspot. The system can be configured to allow the user to correct the user-identified geofence to “shrink” or otherwise reshape it to match to the closest or most overlapping Hotspot geofence boundaries.

The system can also be configured to allow a user to give the system permission to correct all user identified geofences to be automatically shrunk or matched to the closest hotspot geofence polygon or boundaries.

In another embodiment, the system can be configured to correct all overlapping geofences, including conflicting user-identified geofences, by matching the boundaries of geofences towards a common respective “Geofence Address” or user-identified point of interest until there is no more overlapping/intersect between the respective Geofences, thereby eliminating non-unique stop POI categorization.

FIGS. 13A-K illustrate a number of geofence conflicts or interactions which the embodiments of the system and methods can be configured to correct.

FIG. 13A shows an example of “Hotspot Straddling” where a Hotspot geofence HS-11 overlaps with two user-identified geofences that are close. In an embodiment, the system can be configured to identify both geofences GF11 and GF 12 as independent frequent stop locations, or to alert the user that two geofences locations are close to a single Hotspot, and to request a manual correction of the geofences GF11, GF 12.

FIGS. 13B-13C show examples of comparable user-identified geofence and Hotspot geofence areas HS-11, with some of the Hotspot geofence area HS-11 outside of the user-identified geofence GF-11. In an embodiment, the system can also be configured to allow the system automatically match the user-defined geofence GF-11 to the Hotspot's geofence polygon or boundaries HS-11 as described herein. Or, a user can be given the option to manually correct the geofence.

FIG. 13D shows an example of a relatively large user-identified geofence GF-11 overlapping and mostly engulfing a smaller Hotspot geofence HS-11, although the Hotspot geofence HS-11 includes a small area outside of the user-identified geofence GF-11. In an embodiment, the system can be configured to allow the system automatically match the larger user-defined geofence GF-11 to the Hotspot's geofence polygon or boundaries HS-11 as described herein, the latter being more accurate to the location for the vehicle stops or based on location information indicating the larger portion of the user-identified geofence GF-11 is not associated with an address or travel route. Or, a user can be given the option to manually correct the geofence.

FIG. 13E shows an example of a plurality of Hotspots HS-I, HS-N, a number which Hotspot geofences HS-I overlap or fall inside the same Geofence GF-11 while other Hotspot geofences HS-N fall outside the user-defined geofence GF-11. In an embodiment, the system can be configured to allow the system automatically resize the user-defined geofence GF-11 to form boundaries around multiple hotspot geofences HS-I, to include those that overlap and are associated with the same address, point of interest, or location reference. The user-defined geofence GF-11 can also be “split” to match overlapping or internal hotspot geofences HS-O with different addresses or location reference identifications. Hotspot geofences outside the user identified geofence GF-11 can be left as is, or if an address or location reference is the same, the corrected geofence can be expanded to include the hotspot HS-N. Or, a user can be given the option to manually correct the geofence.

FIGS. 13F-13I show overlapping user-identified geofences with completely contained Hotspot geofences. In FIG. 13F, a first user-defined geofence GF11 overlaps a second user defined geofence GF-12. In the first user-defined geofence is a first hotspot geofence HS-11 while in the second user-defined geofence GF12 is a second hotspot HS-12, the second user-defined geofence GF12 also intersecting with the first user-defined geofence GF11 at an overlap boundary. The system can be configured to correct the user defined geofences to match the hotspot geofences that are closest to the center area of the respective geofences, thus the first user-identified geofence can be corrected to match the first hotspot geofence HS11 and the second user defined geofence can be corrected to match the second hotspot geofence HS-12. Or, a user can be given the option to manually correct the geofence.

In FIG. 13G, a first user-defined geofence GF11 overlaps a second user defined geofence GF12, however in the overlapping area both user-defined geofences contain a single hotspot geofence HS11. In an embodiment, the system can be configured to correct the user defined geofences to match the Hotspot geofence HS-11 that is closest to the center area of the user-identified geofences, thus the second user-identified geofence GF-12 can be corrected to match the first hotspot geofence HS-11. Or, a user can be given the option to manually correct the geofence.

In FIG. 13H a first user-defined geofence GF11 overlaps a second user-defined geofence GF-12. A Hotspot geofence HS-11 is inside the second user-defined geofence GF-12 and just touching the overlapping area with the second user-defined geofence GF-11. In an embodiment, the system can be configured to correct the user defined geofences to match the Hotspot geofence HS-11 to the that is closest to the center area of the user-identified geofence and/or the user-identified geofence is shares the most area with. Thus the second user-identified geofence GF-12 can be corrected to match the first hotspot geofence HS-11. Or, a user can be given the option to manually correct the geofence.

In FIG. 13I a first user-defined geofence GF11 overlaps a second user-defined geofence GF-12. In the first user-defined geofence GF-11 is a first Hotspot geofence HS-11 while in the second user-defined geofence GF-12 is a second Hotspot geofence HS-12, the second Hotspot geofence HS-12. A third user-defined geofence GF-13 overlaps the first user-defined geofence GF-11, and the third user geofence GF-13 slightly overlaps the first hotspot geofence HS-11. The system can be configured to correct the user-defined geofences to match the hotspot geofences that are closest to the center area of the respective geofences, thus the first user-identified geofence GF-11 can be corrected to match the first hotspot geofence HS11 and the second user defined geofence can be corrected to match the second hotspot geofence HS-12. The third user-defined geofence GF-13 will not intersect or conflict with the first user-defined geofence GF-11 after the correction. Or, a user can be given the option to manually correct the geofence.

FIG. 13J shows an example of a relatively large user-identified geofence GF11 only slightly overlapping a smaller Hotspot geofence HS-11, with about 60 to 70% of the Hotspot geofence HS-11 being outside of the user-identified geofence GF-11. In an embodiment, the system can be configured to allow the system automatically enlarge the user-defined geofence to encompass the Hotspot's geofence polygon or boundaries if, for example, location information indicates the user-identified geofence GF-11 is associated with a single address or location. Or, a user can be given the option to manually correct the geofence.

FIG. 13K shows an example of a Hotspot geofence HS-11 which overlaps with a user-defined geofence GF-11 which a user has defined as an employee home “Fidel's House.” in an embodiment, the system can be configured to employ a “Home” location type categorization algorithm as described herein to automatedly determine if the hotspot meets the criteria for a “Home” location-type categorization and, if so, correct the geofencing by expanding or matching the geofence GF-11 to hotspot HS-11. If, on the other hand, the hotspot does not meet the criteria for a home location-type categorization, the system can be configured to shrink the geofence GF-11 to match the address and not overlap the hotspot geofence HS-11.

Trend Analysis

As explained above, the computer system includes a GPS event database. The GFS event data is analyzed to derive a plurality of operational metrics for each of a plurality of vehicles; and can also be configured to identify, from the analysis, at least one trend for a GPS event history using the GPS event data. The system can be configured to provide to a graphic user interface an interactive display configured display a representation (e.g., a graphic presentation of the trend). Reports and alerts based on GPS data and tracking and trending can be provided, including for frequent stop locations, driving behavior including vehicle speed (including average speed and high speed), engine start-up and shut-down, idling time, number of stops or any others including as described herein A description of systems and methods for accumulating and presenting data for GPS data for a fleets and vehicles, including for operational metrics and trends, can be found in U.S. patent application Ser. No. 13/097,689 entitled SYSTEM AND METHOD FOR PROVIDING VEHICLE AND FLEET PROFILES AND PRESENTATIONS OF TRENDS, the entirety of which is incorporated by reference herein.

FIGS. 14A-14C shows a graphic flow for analyzing received GPS event data and GPS data stored over time to identify, report, and display trending. As shown in FIG. 14A, GPS event data for each vehicle, as described herein, is gathered over a period of time. The data is analyzed for over time (shown over 6 months) derive operational metrics 302, for example, mileage 302 b, speeding, 302 d and idling 302 a. Trending can also be derived and displayed for other operational metrics as described herein or other such metrics as are or become available using data from GPS events and external databases. Such data can be analyzed for each month to identify statistics for each operational metric, for example, for each vehicle or employee, as well as to derive statistic for the fleet (e.g. averages under each operational metric, or averages for defined groups under each metric, frequent stop locations). Such data can be collected and stored indefinitely.

As shown in FIG. 14B, the analyzed historical event data for each operational metric 302 shows trend data 362, for example, the performance of a vehicle/driver v over the 6 month period, for example, a mileage trend 362 b, a speeding trend, 362 d and an idling trend 362 a for each month. As shown in FIG. 14C, the trend data 362 for each operational metric can then be extrapolated from the GPS event data. This trend data is extrapolated for each vehicle in a fleet, as well as for each employee, and can be used in conjunction with other databases to provide trending and statistical data as described herein. Trend data includes not only the direction in which performance and behavior move under operational metrics, but also identifiable changes in those movements and comparisons therebetween, as well as statistical data drawn from GPS data and other databases, as described herein.

Systems and modules described herein may comprise software, firmware, hardware, or any combination(s) of software, firmware, or hardware suitable for the purposes described herein. Software and other modules may reside on servers, workstations, personal computers, computerized tablets, PDAs, and other devices suitable for the purposes described herein. Software and other modules may be accessible via local memory, via a network, via a browser or other application in an ASP context, or via other means suitable for the purposes described herein. Data structures described herein may comprise computer files, variables, programming arrays, programming structures, or any electronic information storage schemes or methods, or any combinations thereof, suitable for the purposes described herein. User interface elements described herein may comprise elements from graphical user interfaces, command line interfaces, and other interfaces suitable for the purposes described herein. Except to the extent necessary or inherent in the processes themselves, no particular order to steps or stages of methods or processes described in this disclosure, including the Figures, is implied. In many cases the order of process steps may be varied, and various illustrative steps may be combined, altered, or omitted, without changing the purpose, effect or import of the methods described.

Accordingly, while the invention has been described and illustrated in connection with preferred embodiments, many variations and modifications as will be evident to those skilled in this art may be made without departing from the scope of the invention, and the invention is thus not to be limited to the precise details of methodology or construction set forth above, as such variations and modification are intended to be included within the scope of the invention. Therefore, the scope of the appended claims should not be limited to the description and illustrations of the embodiments contained herein. 

What is claimed is:
 1. A computer system of a vehicle tracking system, the computer system including at least one computer processor and non-transitory computer readable storage medium or media including computer code and at least one storage device in which is stored GPS data for at least one vehicle, the computer system comprising: a memory including a GPS event database including GPS event data transmitted from at least one GPS device to the computer system, each GPS device associated with a vehicle, and stored over a period of time; the one or more processors programmed at least to receive GPS event data transmitted from at least one GPS device to the computer system, each GPS device associated with a vehicle, the GPS event data including GPS location information for the vehicle; store the GPS event data in a GPS event database operatively coupled to at least one of the processors; analyze the GPS event data to determine vehicle stops for at least one vehicle; determine if the vehicle stops meets a location type identification criterion for a geofence; if the criterion is met, automatedly categorize the geofence with respect to a location type; and wherein the computer system is configured to wirelessly communicate with the GPS device of the vehicle and to perform vehicle management activities with respect to the vehicle based on the vehicle location relative to the geofence.
 2. The computer system of claim 1, wherein the geofence and wherein the boundary shape for the geofence is at least one of a boundary shape for an automatedly identified frequent stop location, a boundary shape for a user identified point of interest, and a boundary shape for predetermined point of interest.
 3. The computer system of claim 1 or 2, wherein the one or more processors are further programmed at least to: automatedly categorize the location type, the location type comprising a location type being selected from the group of: an office location type, a depot location type, and a home location type.
 4. The computer system of claim 3, wherein the one or more processors are further programmed at least to determine if the stops meet a location type identification criterion for the area by at least: determining vehicle stop characteristics for the area; and determining if the vehicle stop characteristics for the area meet a location type identification criterion.
 5. The computer system of claim 4, wherein the one or more processors are further programmed at least to determine the vehicle stop characteristics by at least: identify a total number of stops for at least one fleet of vehicles; identify and sort the stops in order of the geofences with the highest average percentage of stops of the total stops for each geofence; identify one or more of the geofences having the highest average percentage of stops as the highest ranked areas; determine the variability of the stops for each of the highest ranked geofences; identify, for each of the highest ranked areas, the number of stops that are last stop for a vehicle for that vehicle for a time period; and identify, for each of the highest ranked areas, a unique vehicle count for the stops.
 6. The computer system of claim 5, wherein the geofences are boundary shapes for automatedly identified frequent stop locations.
 7. The computer system of claim 5, wherein the identification of a total number of stops for at least one fleet of vehicles is identified and sorted by a predefined geographical region.
 8. The computer system of claim 5, wherein the one or more processors are further programmed to identify 7 to 10 geofences comprising the highest average percentage of stops.
 9. The computer system of claim 6, wherein the determination of variability comprises calculating the standard deviation across the identified highest ranked geofences.
 10. The computer system of claim 5, wherein the one or more processors are further programmed to determine if the vehicle stop characteristics for the geofence a location type identification criterion by at least: selecting one or more of the identified highest ranked geofences for categorization; and determining if the identified highest ranked geofences meet a threshold number of the location type identification criteria.
 11. The computer system of claim 10, wherein the criteria comprise one or more of: (1) a threshold coefficient of variation; (2) a minimum percentage of the stops in the geofence of the total stops for the number of stops identified for the geographic region; (3) the number of stops for the geofence is a minimum number of stops for a predetermined time period; (4) at least one of the stops for the geofence is the last stop for a vehicle; and (5) wherein a threshold number of stops are stops per unique vehicles.
 12. The computer system of claim 11, wherein the vehicle stop characteristics for the geofence must meet a threshold number of the location type identification criteria.
 13. The computer system of claim 12, wherein the threshold number location type identification criteria is in the range of 2 out of 5 criteria to 5 out of 5 criteria.
 14. The computer system of claim 12, wherein the number of geofences identified as having the highest number of stops is 7, and the threshold coefficient of variation is selected from the range of about at least 0.5 to at least about 0.7.
 15. The computer system of claim 12, wherein the minimum percentage of the stops is at least 10% of the total stops for the number of stops for the fleet.
 16. The computer system of claim 12, wherein the threshold number of unique stops is at least 30 stops per unique vehicle.
 17. The computer system of claim 13, wherein for the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the system is configured to at least: select only a first highest ranked geofence, whereby the first highest ranked is categorized as an office location type.
 18. The computer system of claim 13, wherein for the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the system is configured to at least: select a first highest ranked geofence to a second highest ranked geofence, whereby the first highest ranked geofence is categorized as an office location type and the second highest ranked geofence is categorized as a depot location type or an office location type.
 19. The computer system of claim 4, wherein the one or more processors are further programmed at least to determine if the geofence meets a home location type identification criterion for the geofence by at least: identifying at least one geofence associated with a single vehicle; determining, for each geofence associated with a single vehicle, a number of stops for the geofence, wherein the number of stops over a time period are the last stop of the day for a predetermined time period; determining a duration of time for each of the number of stops over a time period are that are the last stop of the day; and wherein the location type criterion comprises criteria including: a minimum number of vehicle stops in the geofence that are the last stop of the day for the single vehicle; a minimum number of vehicle stops in the geofence that are the last stop of the day; and an address designation for a minimum percentage of stops within the geofence is designated as a qualified home address.
 20. A method executed by a computer system of a vehicle tracking system, the computer system comprising at least one computer processor and a computer readable non-transitory storage medium or media including computer code, the method comprising: receiving GPS event data transmitted from a plurality of GPS devices to the computer system, each GPS device associated with a vehicle, the GPS event data including GPS location information for the vehicle; storing, in a GPS event database operatively coupled to at least one of the processors, the GPS event data; analyzing the GPS event data to derive and record vehicle stops for at least one vehicle; determine if the vehicle stops meets a location type identification criterion for a geofence; if the criterion is met, automatedly categorize the geofence with respect to a location type; and wherein the computer system wirelessly communicates with each of the plurality of GPS devices and performs vehicle management activities with respect to the vehicle based on the vehicle location relative to the geofence.
 21. The method of claim 20, wherein the geofence comprises at least one of a geofence for an automatedly identified frequent stop location, a boundary shape for a user identified point of interest, and a boundary shape for predetermined point of interest.
 22. The method of claim 20 or 21, further comprising: automatedly categorizing the location type, the location type comprising a location type being selected from the group of: an office location type, a depot location type, and a home location type.
 23. The method of claim 22, further comprising: determining vehicle stop characteristics for the area; and determining if the vehicle stop characteristics for the area meet a location type identification criterion.
 24. The method of claim 23, further comprising: identifying a total number of stops for at least one fleet of vehicles; identifying and sorting the stops in order of the geofences with the highest average percentage of stops of the total stops for each geofence; identifying one or more of the geofences having the highest average percentage of stops as the highest ranked areas; determining the variability of the stops for each of the highest ranked geofences; identifying, for each of the highest ranked areas, the number of stops that are last stop for a vehicle for that vehicle for a time period; and identifying, for each of the highest ranked areas, a unique vehicle count for the stops.
 25. The method of claim 24, wherein the geofences are boundary shapes for automatedly identified frequent stop locations.
 26. The method of claim 24, wherein the identification of a total number of stops for at least one fleet of vehicles is identified and sorted by a predefined geographical region.
 27. The method of claim 26, further comprising: identifying 7 to 10 geofences comprising the highest average percentage of stops.
 28. The method of claim 25, wherein the determination of variability comprises calculating the standard deviation across the identified highest ranked geofences.
 29. The method of claim 24, further comprising: selecting one or more of the identified highest ranked geofences for categorization; and determining if the identified highest ranked geofences meet a threshold number of the location type identification criteria.
 30. The method of claim 29, wherein the criteria comprise one or more of: (1) a threshold coefficient of variation; (2) a minimum percentage of the stops in the geofence of the total stops for the number of stops identified for the geographic region; (3) the number of stops for the geofence is a minimum number of stops for a predetermined time period; (4) at least one of the stops for the geofence is the last stop for a vehicle; and (5) wherein a threshold number of stops are stops per unique vehicles.
 31. The method of claim 30, wherein the vehicle stop characteristics for the geofence must meet a threshold number of the location type identification criteria.
 32. The method of claim 31, wherein the threshold number location type identification criteria is in the range of 2 out of 5 criteria to 5 out of 5 criteria.
 33. The method of claim 32, wherein the number of geofences identified as having the highest number of stops is 7, and the threshold coefficient of variation is selected from at least about 0.5 to at least about 0.7.
 34. The method of claim 32, wherein the minimum percentage of the stops is at least 10% of the total stops for the number of stops for the fleet.
 35. The method of claim 32, wherein the threshold number of unique stops is at least 30 stops per unique vehicle.
 36. The method of claim 32, wherein for the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the method further comprises: selecting only a first highest ranked geofence, whereby the first highest ranked is categorized as an office location type.
 37. The method of claim 32, wherein for the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the method further comprises: selecting a first highest ranked geofence to a second highest ranked geofence, whereby the first highest ranked geofence is categorized as an office location type and the second highest ranked geofence is categorized as a depot location type or an office location type.
 38. The method of claim 23, wherein the method further comprises determining if the geofence meets a home location type identification criterion for the geofence by at least: identifying at least one geofence associated with a single vehicle; determining, for each geofence associated with a single vehicle, a number of stops for the geofence, wherein the number of stops over a time period are the last stop of the day for a predetermined time period; determining a duration of time for each of the number of stops over a time period are that are the last stop of the day; and wherein the location type criterion comprises criteria including: a minimum number of vehicle stops in the geofence that are the last stop of the day for the single vehicle; a minimum number of vehicle stops in the geofence that are the last stop of the day; and an address designation for a minimum percentage of stops within the geofence is designated as a qualified home address. 